7 Papers Accepted to ICLR 2023

Research papers from the department were accepted to the 11th International Conference on Learning Representations (ICLR 2023). ICLR is the premier conference on deep learning where researchers gather to discuss their work in the fields of artificial intelligence, statistics, and data science. 

Notable, top 5%

Visual Classification via Description from Large Language Models
Sachit Menon Columbia University, Carl Vondrick Columbia University

Keywords: vision-language models, CLIP, prompting, GPT-3, large language models, zero-shot recognition, multimodal

TL;DR: We enhance zero-shot recognition with vision-language models by comparing to category descriptors from GPT-3, enabling better performance in an interpretable setting that also allows for the incorporation of new concepts and bias mitigation.

Vision-language models such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure — computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich context of additional information that language affords. The procedure gives no intermediate understanding of why a category is chosen and furthermore provides no mechanism for adjusting the criteria used towards this decision. We present an alternative framework for classification with VLMs, which we call classification by description. We ask VLMs to check for descriptive features rather than broad categories: to find a tiger, look for its stripes; its claws; and more. By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used. In the process, we can get a clear idea of what the model “thinks” it is seeing to make its decision; it gains some level of inherent explainability. We query large language models (e.g., GPT-3) for these descriptors to obtain them in a scalable way. Extensive experiments show our framework has numerous advantages past interpretability. We show improvements in accuracy on ImageNet across distribution shifts; demonstrate the ability to adapt VLMs to recognize concepts unseen during training; and illustrate how descriptors can be edited to effectively mitigate bias compared to the baseline.


Notable, top 25%

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
Peter Yichen Chen Columbia University, Jinxu Xiang Columbia University, Dong Heon Cho Columbia University, Yue Chang University of Toronto, G A Pershing Columbia University, Henrique Teles Maia Columbia University, Maurizio M Chiaramonte Meta Reality Labs Research, Kevin Thomas Carlberg Meta Reality Labs Research, Eitan Grinspun University of Toronto

Keywords: PDE, implicit neural representation, neural field, latent space traversal, reduced-order modeling, numerical methods

TL;DR: We accelerate PDE solvers via rapid latent space traversal of continuous vector fields leveraging implicit neural representations.

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas prior ROM approaches reduce the dimensionality of discretized vector fields, our continuous reduced-order modeling (CROM) approach builds a low-dimensional embedding of the continuous vector fields themselves, not their discretization. We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations. We validate our approach on an extensive range of PDEs with training data from voxel grids, meshes, and point clouds. Compared to prior discretization-dependent ROM methods, such as linear subspace proper orthogonal decomposition (POD) and nonlinear manifold neural-network-based autoencoders, CROM features higher accuracy, lower memory consumption, dynamically adaptive resolutions, and applicability to any discretization. For equal latent space dimension, CROM exhibits 79x and 49x better accuracy, and 39x and 132x smaller memory footprint, than POD and autoencoder methods, respectively. Experiments demonstrate 109x and 89x wall-clock speedups over unreduced models on CPUs and GPUs, respectively. Videos and codes are available on the project page: https://crom-pde.github.io



Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions
Jake Snell Princeton University, Thomas P Zollo Columbia University, Zhun Deng Columbia University, Toniann Pitassi Columbia University, Richard Zemel Columbia University

Keywords: distribution-free uncertainty quantification

TL;DR: We propose a framework to rigorously and flexible control the quantiles of the loss distribution incurred by a predictor or set of predictors.

Rigorous guarantees about the performance of predictive algorithms are necessary in order to ensure their responsible use. Previous work has largely focused on bounding the expected loss of a predictor, but this is not sufficient in many risk-sensitive applications where the distribution of errors is important. In this work, we propose a flexible framework to produce a family of bounds on quantiles of the loss distribution incurred by a predictor. Our method takes advantage of the order statistics of the observed loss values rather than relying on the sample mean alone. We show that a quantile is an informative way of quantifying predictive performance, and that our framework applies to a variety of quantile-based metrics, each targeting important subsets of the data distribution. We analyze the theoretical properties of our proposed method and demonstrate its ability to rigorously control loss quantiles on several real-world datasets.

Causal Imitation Learning via Inverse Reinforcement Learning
Kangrui Ruan Columbia University, Junzhe Zhang Columbia University, Xuan Di Columbia University, Elias Bareinboim Columbia University

Keywords: Causal Inference, Graphical Models

TL;DR: This paper proposes novel inverse reinforcement learning methods to learn effective imitating policies from the expert’s demonstrations when unobserved confounders are present.

One of the most common ways children learn when unfamiliar with the environment is by mimicking adults. Imitation learning concerns an imitator learning to behave in an unknown environment from an expert’s demonstration; reward signals remain latent to the imitator. This paper studies imitation learning through causal lenses and extends the analysis and tools developed for behavior cloning (Zhang, Kumor, Bareinboim, 2020) to inverse reinforcement learning. First, we propose novel graphical conditions that allow the imitator to learn a policy performing as well as the expert’s behavior policy, even when the imitator and the expert’s state-action space disagree, and unobserved confounders (UCs) are present. When provided with parametric knowledge about the unknown reward function, such a policy may outperform the expert’s. Also, our method is easily extensible and allows one to leverage existing IRL algorithms even when UCs are present, including the multiplicative-weights algorithm (MWAL) (Syed & Schapire, 2008) and the generative adversarial imitation learning (GAIL) (Ho & Ermon, 2016). Finally, we validate our framework by simulations using real-world and synthetic data.

Neural Causal Models for Counterfactual Identification and Estimation
Kevin Muyuan Xia Columbia University, Yushu Pan Columbia University, Elias Bareinboim Columbia University

Keywords: causal inference, deep learning, neural models, neural causal models, causal identification, causal estimation, counterfactual

TL;DR: We solve the two problems of counterfactual identification and estimation from arbitrary surrogate experiments using a Generative Adversarial Network implementation of the Neural Causal Model.

Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this paper, we study the evaluation of counterfactual statements through neural models. Specifically, we tackle two causal problems required to make such evaluations, i.e., counterfactual identification and estimation from an arbitrary combination of observational and experimental data. First, we show that neural causal models (NCMs) are expressive enough and encode the structural constraints necessary for performing counterfactual reasoning. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions. We show that this algorithm is sound and complete for deciding counterfactual identification in general settings. Third, considering the practical implications of these results, we introduce a new strategy for modeling NCMs using generative adversarial networks. Simulations corroborate with the proposed methodology.

Understanding Zero-shot Adversarial Robustness for Large-Scale Models
Chengzhi Mao Columbia University, Scott Geng Columbia University, Junfeng Yang Columbia University, Xin Wang Microsoft Research, Carl Vondrick Columbia University

Keywords: Adversarial Robustness, Zero-Shot Recognition

Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce CLIP’s performance on new tasks. In this work, we identify and explore the problem of adapting large-scale models for zero-shot adversarial robustness. We first identify two key factors during model adaption–training losses and adaptation methods–that affect the model’s zero-shot adversarial robustness. We then propose a text-guided contrastive adversarial training loss, which aligns the text embeddings and the adversarial visual features with contrastive learning on a small set of training data. We apply this training loss to two adaption methods, model finetuning and visual prompt tuning. We find that visual prompt tuning is more effective in the absence of texts, while finetuning wins in the existence of text guidance. Overall, our approach significantly improves the zero-shot adversarial robustness over CLIP, seeing an average improvement of 31 points over ImageNet and 15 zero-shot datasets. We hope this work can shed light on understanding the zero-shot adversarial robustness of large-scale models.

TempCLR: Temporal Alignment Representation with Contrastive Learning
Yuncong Yang Columbia University, Jiawei Ma Columbia University, Shiyuan Huang Columbia University, Long Chen Columbia University, Xudong Lin Columbia University, Guangxing Han Columbia University, Shih-Fu Chang Columbia University

Keywords: Representation learning, Global Sequence Alignment, Zero/Few-shot Transfer

TL;DR: Global sequence matching under temporal order consistency matters in contrastive-based video-paragraph/text learning.

Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a common feature space. For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly. However, such unit-level similarity measure may ignore the global temporal context over a long time span, which inevitably limits the generalization ability. In this paper, we propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. As the video/paragraph is formulated as a sequence of clips/sentences, under the constraint of their temporal order, we use dynamic time warping to compute the minimum cumulative cost over sentence-clip pairs as the sequence-level distance. To explore the temporal dynamics, we break the consistency of temporal order by shuffling the video clips or sentences according to the temporal granularity. In this way, we obtain the representations for clips/sentences, which perceive the temporal information and thus facilitate the sequence alignment. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between different video instances. We evaluate our approach on video retrieval, action step localization, and few-shot action recognition, and achieve consistent performance gain over all three tasks. Detailed ablation studies are provided to justify the approach design.

Voices of CS: Gaurav Jain

The third-year PhD student is creating tools to help people with vision impairments navigate the world.


Imagine walking to your office from the subway station on a Monday morning. You notice a new café on the way, so you decide to take a detour and try a latté. That sounds like a normal way to start the week, right?

But for people with vision impairment or low vision, like those who are categorized as blind and low vision (BLV), this kind of spontaneous exploration while outside is challenging. Current navigation assistance systems (NAS) provide turn-by-turn instructions, but they do not allow visually impaired users to deviate from the shortest path to their destination or make decisions on the fly. As a result, people with vision impairment or low vision often miss out on the freedom to go out and navigate on their own terms.

Gaurav Jain

In a paper published at the ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW ‘23), computer science researchers introduced the concept of “Exploration Assistance,” which is an evolution of current NASs that can support BLV people’s exploration in unfamiliar environments. Led by Gaurav Jain, the researchers investigated how NASs should be designed by interviewing BLV people, orientation and mobility instructors, and leaders of blind-serving organizations, to understand their specific needs and challenges. Their findings highlight the types of spatial information required for exploration beyond turn-by-turn instructions and the difficulties faced by BLV people when exploring alone or with the help of others.

Jain, who is advised by Assistant Professor Brian Smith, is a PhD student in the Computer-Enabled Abilities Laboratory (CEAL Lab), where researchers develop computers that help people perceive and interact with the world around them. Their paper presents the results of interviews with BLV people and other stakeholders to identify the types of spatial information BLV people need for exploration and the challenges BLV people face when exploring unfamiliar environments. The paper offers insights into the design and development of new navigation assistance systems that can support BLV people in exploring unfamiliar environments with greater spontaneity and agency.

The study investigates how navigation assistance should evolve to support blind people in exploring unfamiliar environments. Traditional approaches, as shown on the left, focus solely on guiding users to their destination. Our findings, as shown on the right, reveal that navigation systems can support exploration in three ways: by conveying area shapes, by conveying the layout of objects, and by facilitating effective collaboration with other people (both blind and sighted), who can "unlock" additional avenues of exploration for the user.
The study investigates how navigation assistance should evolve to support blind people in exploring unfamiliar environments. Traditional approaches, as shown on the left, focus solely on guiding users to their destination. The group’s findings, as shown on the right, reveal that navigation systems can support exploration in three ways: by conveying area shapes, by conveying the layout of objects, and by facilitating effective collaboration with other people (both blind and sighted), who can “unlock” additional avenues of exploration for the user.


Based on their findings, they presented several instances of NASs that support the exploration assistance paradigm and identify several challenges that need to be overcome to make these systems a reality. Jain hopes that his research will ultimately enable BLV people to experience greater agency and independence as they navigate and explore their environments. We sat down with Jain to learn more about his research, doing qualitative research, and the thought processes behind writing research papers.


Q: What is exploration assistance and why is it important to do research on it?

This research is incredibly exciting for the blind and low vision (BLV) community, as it represents a significant step towards equal access and agency in exploring unfamiliar environments. For BLV people, the ability to navigate and explore independently is essential to daily life, and current navigation assistance systems often limit their ability to do so. By introducing the concept of exploration assistance, this research opens up new possibilities for BLV people to explore and discover their surroundings with greater spontaneity and freedom. This research has the potential to significantly improve the quality of life for BLV people and is a major development in the ongoing pursuit of accessibility and inclusion for all.


Q: How did you become part of the research project?

This was my first project as a PhD student in the CEAL lab. The project was initiated as a camera-based wearable NAS for BLV people, and we conducted several formative studies with BLV people.

As we progressed, we realized that there was a significant research gap in the research community’s understanding of how NASs could support BLV people’s exploration in navigation. Based on these findings, we shifted our focus toward investigating this gap, and the paper I worked on was the result of this pivot. The paper is titled, “I Want to Figure Things Out”: Supporting Exploration in Navigation for People with Visual Impairments.


Q: The research was more qualitative, right? How did you find working on it?

Over the course of approximately one year, I had the opportunity to work on this project that challenged me to step outside of my comfort zone as a human-computer interaction (HCI) researcher. Before this project, my research experience had primarily focused on computer vision and deep learning. I was more at ease with HCI systems research, which involved designing, building, and evaluating tools and techniques to solve user problems.

This project, however, was a qualitative research study that aimed to gain a deeper understanding of user needs, behaviors, challenges, and attitudes toward technology through in-depth interviews, observations, and other qualitative data collection methods. To prepare for this project, I had to immerse myself in the field of accessibility and navigation assistance for BLV people and read extensively on papers that employed qualitative research methods.

Although it took some time for me to shift my mindset towards qualitative research, this project helped me become a more well-rounded researcher, as I now feel comfortable with both qualitative and systems research. Overall, this project was a significant personal and professional growth experience, as I was able to expand my research expertise and contribute to a worthy cause.


Q: Can you talk about the process of writing the paper? When it came time to start writing, how did you organize your thoughts and the data?

Writing the paper was a critical stage in the research process, and I approached it by first organizing my thoughts and drafting a clear outline. I started by creating an outline of the paper with section and subsection headers, accompanied by a brief summary of what I intended to discuss in each section. This process allowed me to see the overall structure of the paper and ensure that I covered all the essential elements.

Once I had a clear structure in mind, I began to tackle each section of the paper one by one, starting with the introduction and then moving on to the methods, results, and discussion sections. I iteratively refined my writing based on feedback from my advisor, lab mates, and friends.

Throughout the writing process, I also ensured that my writing was clear, concise, and easy to follow. I paid close attention to the flow of ideas and transitions between sections, making sure that each paragraph and sentence contributed to the overall argument and was well-supported by the evidence.

Overall, the process of writing the paper was challenging but rewarding. It allowed me to synthesize the research findings and present them in a compelling way, showcasing the impact of our work on the lives of BLV people.


Q: What did you find surprising or challenging about the process?

Throughout the research process, I encountered various challenges that both surprised and tested me. Interviewing participants, in particular, proved to be an intriguing yet difficult task. Initially, I struggled to guide conversations naturally toward my research questions without leading participants toward a certain answer. However, with each interview, I became more confident and began to enjoy the process. Hearing firsthand from BLV people that our work could make a real impact on their lives was also incredibly rewarding.

Analyzing and synthesizing the interview data was another major challenge. Unlike quantitative data, conversations are often open-ended and context-dependent, making it difficult to separate my own biases from the interviewee’s responses. I spent a considerable amount of time reviewing the interview transcripts and identifying emerging themes. To facilitate this process, I leveraged tools like NVivo to better organize the interview data, and our team held several discussions to refine these themes. To ensure the accuracy of our interpretation, we sought feedback from two BLV interns who worked with us over the summer on another project.

Overall, this research experience pushed me to become more adaptable. While it presented its own unique set of challenges, I am proud to have contributed to a project that has the potential to create meaningful change in the lives of BLV people.


Jain working with a summer intern in the CEAL Lab.


Q: Did it change your view on how you should do research?

Yes, my experience with this research project has certainly changed my view on how to approach research. It has taught me the importance of keeping the paper in mind from the beginning of a project.

Now, I make a conscious effort to think about how I want to present my work and what story I want to tell with the research. This helps me gain more clarity on the direction of the project and how to steer it toward producing meaningful results. As part of my workflow, I now write early drafts of paper introductions even before developing any tools or systems. This allows me to zoom out from the day-to-day technical challenges and see the big picture, which is crucial in making sure that the research is both impactful and well-presented.


Q: What are your tips for writing a research paper?

Writing a research paper can be a challenging task, but here are a few tips that have helped me make the process smoother:

  1. Start with a rough draft: Don’t expect your first draft to be perfect. It’s important to just start writing and get your ideas on paper. You can always revise and edit later. Use a tool like Microsoft Word or Google Docs to get started instead of working directly with Overleaf. I found this to take the pressure off me.
  2. Observe your advisor’s edits: Your advisor can be a valuable resource when it comes to writing. Observing your advisor edit your drafts can help you learn from their feedback. I usually ask my advisor, Brian Smith, to describe why he made a certain edit and that helps me understand his process and also identify specific issues where I need to work more on.
  3. Get feedback and revise: It’s important to get feedback on your paper from others. Share your draft with colleagues, friends, and family, and ask for their honest feedback. Use their feedback to revise and improve your paper. Whenever I’m writing, I socialize my writing with others, including my advisor, my lab mates, my friends, and my family. Interestingly, I get the most useful feedback from my friends and family, who have no idea what my research is about. I ask them to describe what they understood from the text I shared and try to match their description with my intended purpose. Writing is an iterative process; it takes several drafts before you have a polished paper.

Finally, one resource that I would totally recommend to every PhD student at Columbia is Adjunct Professor Janet Kayfetz’s class on Technical Writing. Her class is an excellent way to deeply understand research writing.


Q: What are you working on now?

I am currently working on two exciting projects that further my research goal of developing inclusive physical and digital environments for BLV people. The first project involves enhancing the capabilities of smart streets, streets with sensors like cameras and computing power, to help BLV people navigate street intersections safely.

This project is part of the NSF Engineering Research Center for Smart Streetscapes’ application thrust. The second project is focused on making videos accessible to BLV people by creating high-quality audio descriptions available at scale.


Q: Can you talk about your background and why you decided to pursue a PhD?

My exposure to research during my undergrad was invaluable, as it allowed me to work on diverse projects utilizing computer vision for various applications such as biometric security and medical imaging. These experiences instilled in me a passion for the research process. It was fulfilling to be able to identify problems that I care about, explore solutions, and disseminate new knowledge.

While I knew I enjoyed research, it was during the summer research fellowship at the Indian Institute of Sciences, where I collaborated with Professor P. K. Yalavarthy in the Medical Imaging Group, that crystallized my decision to pursue a PhD. The opportunity to work in a research lab, lead a project, and receive mentorship from an experienced advisor provided a glimpse of what a PhD program entails. I was excited by the prospect of being able to make a real-world impact by solving complex problems, and it was then that I decided to pursue a career in research.


Q: How has your research interest changed since you started your PhD?

I am interested in building Human-AI systems that embed AI technologies (e.g., computer vision) into human interactions to help BLV people better experience the world around them. My work on exploration assistance informs the design of future navigation assistance systems that enable BLV people to experience the physical world with more agency and spontaneity during navigation.

In addition to the physical world, I’ve also broadened my research focus to enhance BLV people’s experiences within the digital world. For example, I developed a system that makes it possible for BLV people to visualize the action in sports broadcasts rather than relying on other people’s descriptions of the game.


Q: What sort of research questions do you hope to answer now?

Accessibility research has traditionally focused on aiding daily-life activities and providing access to digital information for productivity and work, but there’s an increasing realization that providing access to everyday cultural experiences is equally important for inclusion and well-being.

This encompasses various forms of entertainment and recreation, such as watching TV, exploring museums, playing video games, listening to music, and engaging with social media. Ensuring that everyone has equal opportunities to enjoy these experiences is an emerging challenge. My goal is to design human-AI systems that enhance such experiences.


Q: Why did you choose to apply to Columbia CS? What attracted you to the program?

I was drawn to Columbia CS because of the type of problems my advisor works on. His research focused on creating systems that have a direct impact on people’s lives, where evaluating the user’s experience with the system is a key component.

This was a departure from my undergraduate research, where I focused on building systems to achieve high accuracy and efficiency. I found this user-centered approach to be extremely exciting, especially in the context of his project “RAD,” which aimed to make video games accessible to blind gamers. It was a super exciting prospect to be working on similar problems where you can firsthand see how people reacted and benefited from your solutions. This still remains one of the most fulfilling aspects of HCI research for me. In the end, this is what led me to choose Columbia and work with Brian Smith.


Jain at the ASSETS 2022 conference in Athens, Greece.


Q: What has been the highlight of your time at Columbia?

The first thing that comes to mind is the people that I have had the pleasure of working with and meeting. I am grateful for the opportunity to learn from my advisor and appreciate the incredible atmosphere he has created for me to thrive.

Additionally, I have been fortunate enough to make some amazing friends here at Columbia who have become a vital support system. Balancing work with passions outside of work has also been important to me, and I am grateful for the chance to engage with student clubs such as the dance team, Columbia Bhangra, and meet some amazing people there as well. Overall, the community at Columbia has been a highlight for me.


Q: What is your advice to students on how to navigate their time at Columbia? If they want to do research, what should they know or do to prepare?

One thing that students wanting to do research should know is that research involves a lot of uncertainty and ambiguity. In fact, dealing with uncertainty can be one of the most challenging aspects of research, even more so than learning the technical skills required to complete a project.

In my own experience, staying motivated about the problem statement has been key to powering through those uncertain moments. Therefore, it is important to be true to yourself about what you are really excited about and work on those problems. Ultimately, this approach can go a long way in helping you navigate your time at Columbia and make the most of your research opportunities.


What Should We Expect From ChatGPT?

The chatbot has made waves over the past couple of months for being able to answer queries in a conversational tone. CS professors discuss what it can and cannot do correctly.


OpenAI’s ChatGPT is an artificial intelligence (AI) chatbot that is trained to follow the instruction in a prompt and give a detailed response. It is built upon GPT-3, a type of large language model (LLM) that predicts and generates text. Given a sequence of words, it will predict the word that has the highest probability of following next (kind of like autocomplete). These models are trained on huge datasets that allow them to generate answers to questions. ChatGPT works quickly and gives answers within seconds, and it also learns from every interaction and improves daily.

It can create a letter to your super asking for a repair to be done, write code and fix bugs, and suggest plot summaries for novels. But that does not mean that it is perfect. The problem with LLMs is that they can “hallucinate” and make things up. ChatGPT is guilty of this; some of the answers in its outputs do not even exist. It is also not trained to be truthful and it answers queries with a lot of confidence and authority, which is worrisome.

It is being compared to the last great tech disruption–the internet’s onset in the 1990s. We asked CS professors what the technology could do and how to use the tool the right way.

Vishal Misra
I have been using GPT-3 for over two years now. It is the underlying model behind my cricket search app for ESPN.

The original interface was cumbersome and needed an analyst who could use specialized programming languages to access the answer.

We developed AskCricInfo, which takes human input–questions or search queries–and converts the queries into a structured language like SQL that machines understand. The technology can “translate” the question into a programming language, find the answer, and quickly send it back to the user.

It is an excellent example of the power of underlying technology and what the tool can do. ChatGPT is very interesting. It is the first chatbot that makes “intelligent” and engaging conversations. There are definite use cases for making it a very effective teaching tool. It is up to the instructors to imagine a world where something like ChatGPT will always be available and teach students how to use it effectively. A lot can go wrong with ChatGPT- primarily because of its design that makes it bullshit confidently. But that is a problem with all LLMs, not just ChatGPT.

Kathy McKeown
ChatGPT and the technology it is built on, GPT3, are game changers. With them and the other large language models that have recently emerged, models can now produce extremely fluent text. They are also able to perform a variety of tasks in a way that was not previously possible. And there are many situations in which they can be used to help us do things that we previously found tedious. But they are not able to do as much as we might initially have imagined. They have limitations that are not always immediately obvious, and researchers are taking the time to study them so that we can accurately characterize what they can and cannot do. Nonetheless, they change how we do research in the natural language field and how we build new models to carry out other tasks.

I think we need to learn how to incorporate them into our teaching because they are here to stay. Students know about them and will know how to use them. We will need to think creatively about how to teach students what these tools do well and what they do poorly, when they can be used as a tool to aid in research, and situations in which it would be dishonest. I imagine situations where we ask ChatGPT to answer an essay question, ask the student to critique the essay and identify what it does poorly, and then ask the student to write an essay that goes beyond what ChatGPT can do.

The worst problem with ChatGPT is that it is not always truthful. In fact, it may often lie or hallucinate facts. It does this very fluently, making its lies sound very plausible. This is quite a barrier to use in a real-world situation unless it is used by someone who knows what is correct, what is not, and can sift the good from the bad. Technology constantly changes, though, and it is a problem that researchers are actively working on.

Itsik Pe’er
I asked ChatGPT about AI language models, and this is the output:

AI language models like ChatGPT, its competitors and successors represent a significant advancement in the field of Natural Language Processing (NLP). These models have the ability to generate human-like text based on a massive amount of input data, making them useful for various applications such as chatbots, question-answering systems, and content generation.

However, it is important to note that despite their sophisticated capabilities, they still have limitations and may generate outputs that are inaccurate, biased, or inappropriate. It is crucial to ensure responsible development and deployment of these models to maximize their benefits while minimizing potential harm.

The above demonstrates my thoughts more than I could otherwise express. In particular: it is already useful, but often not out-of-the-box, but I am a techno-optimist and believe even more usefulness is coming.

As a former judge in high school debate competitions, it reminds me of the most eloquent contestants, who would, if they needed to, lie through their teeth without missing a heartbeat. For instance, in response to this prompt, “What could go wrong with tools like ChatGPT?”, it writes:

There are several potential risks associated with the use of AI language models like ChatGPT:
Bias: AI models are trained on large amounts of text data, which may reflect societal biases and lead to biased outputs.
Misinformation: AI models may generate false or misleading information, which could have serious consequences if relied upon.
Privacy Concerns: AI models process large amounts of personal information, raising privacy and security concerns.
Misuse: AI models may be used for malicious purposes, such as spreading hate speech, fake news, or conducting phishing attacks.
Job Loss: AI models may automate certain tasks, leading to job loss in industries that rely on those tasks.
Therefore, it is important to carefully consider the potential risks and consequences of AI models and to develop and deploy them responsibly to minimize potential harm.

These are all valid points, but more context needs to be considered:

  • When it comes to bias, chatbots are worse than the best other (human?) alternatives but much better than the median ones.
  • Misinformation is a big problem when compounded by misuse, and we can see that happening now with stories posted online.
  • Privacy concerns depend on who has access to the data in ChatGPT and what can be done with it. But given that we all carry powerful tracking and sensing devices 24/7, our privacy is already weakly protected against powerful actors.
  • Some attention-seeking blogs and websites overstate the job loss point. Many jobs will become more efficient; many jobs will change; many jobs will be created, and, yes, many will be lost. People will adapt, and we will all be better for it.


Voices of CS: Tuhin Chakrabarty 

PhD student Tuhin Chakrabarty talks about how his research is tapping into the creative side of computer science.


The field of natural language processing (NLP) has ramped up by leaps and bounds. This branch of artificial intelligence focuses on the ability of computers to understand and process language as humans do. It has been in the news these past few months because of a chatbot, ChatGPT, that can provide answers and data conversationally. The technology gives us a taste of just how powerful and useful NLP can be.

Tuhin Chakrabarty wants to see how much further he can push NLP in the field of computational creativity to see how computers can generate creative output. This is what ChatGPT had to say about computational creativity:

Computational creativity is a field that uses computational methods to simulate and enhance human-like creativity, producing valuable outputs such as art, music, stories, and scientific discoveries. It aims to understand and replicate the cognitive processes involved in human creativity, combining techniques from AI, cognitive psychology, and philosophy. Examples of computational creativity include generative art and music, game design, natural language processing, and scientific discovery. Ultimately, computational creativity seeks to leverage computers and algorithms to augment and extend human creativity, creating new possibilities for creative expression and innovation.
Tuhin Chakrabarty
Tuhin Chakrabarty

“Generating text beyond a few sentences was almost very difficult two years ago, but things look much better now. It is not perfect, but I am optimistic,” said Tuhin Chakrabarty, who first became interested in computational creativity in 2019. “One of the things that I am excited about is how better we can align models like ChatGPT to human expectations and different cultures.”

Instead of creating text conversationally, Chakrabarty’s research focuses on how AI can be used to create metaphors and detect sarcasm with little to no training data. The fifth-year PhD student advised by Smaranda Muresan has expanded his work to generating long narratives of 2,000-word documents and visual metaphors. We recently sat down with him to learn more about his research and the creative possibilities of NLP.

Q: You mentioned that you became interested in doing research during your MS. What happened that made you interested in doing research?

I did not have much research experience as an undergrad. I got accepted to the CS masters program and I was fortunate enough to take a class offered by my advisor Smaranda Muresan, which still happens to be one of my all-time favorite courses at Columbia. Computational models of Social Meaning was a graduate seminar course about impactful papers in NLP. Reading all the papers in that class made me think about what I want to do with NLP and how so many interesting research questions can be answered computationally by studying language. Alongside this, I was also working with my advisor and my friend Chris Hidey on extracting arguments from social media. That experience was really precious. The enthusiasm everyone shared in trying to solve the problem at hand made me sure of my decision to pursue research.

Q: How did you become interested in computational creativity? And what is it? 

Around 2019, Nanyun Peng and He He, two very important researchers in the field of computational creativity, wrote a paper on generating puns. I happened to attend NAACL 2019 in Minneapolis, where the paper was presented. I thought the paper was beautiful in every possible way and it quantified the surprisal theory in humor algorithmically. This made me really fascinated about how we can use inductive biases to help machines generate creative output. For selfish reasons, I reached out to Nanyun Peng and told her that I wanted to work with her. She was very kind and agreed to mentor me. My PhD advisor Smaranda Muresan is one of the experts in the field of Figurative Language, which deals with creativity. So, of course, that influenced my decision to work in computational creativity too.

Computational creativity is a multidisciplinary endeavor located at the intersection of artificial intelligence, cognitive psychology, philosophy, and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer to achieve one of several ends:

  • To construct a program or computer capable of human-level creativity.
  • To better understand human creativity and formulate an algorithmic perspective on human creative behavior.
  • To design programs that can enhance human creativity without necessarily being creative themselves.


Q: How can you train a model or algorithm to interpret creativity or language?

State-of-the-art models are often found to be inadequate for creative tasks. The principal reason for this is that in addition to composing grammatical and fluent sentences to articulate given content, these tasks usually require extensive world and common sense knowledge.

It should also be noted that current approaches to text generation require lots of training data for supervision. However, most existing corpus for creative forms of text is limited in size. Even if such a corpus existed, learning the distribution of existing data and sampling from it is unlikely to lead to truly novel, creative output.

So we have to rely on unsupervised or weakly supervised techniques to train an end-to-end model to interpret or generate creative text. Of course, with the advent of Large Language Models and few-shot learning, we can now prompt a model with a few examples of creative text and it can somewhat generalize (but not as well as humans). My dissertation deals with a lot of this.


Q: Let’s talk about your work with the New York Times. What type of research questions did you have to answer while there? How was it different from what you have been doing?

Over the past several years, a key focus for NYTimes Research and Development has understood how advances in machine learning can extend the capabilities of journalists and unlock reader experiences that aren’t possible today. Questions and answers are central to how humans learn. Times journalism frequently uses FAQ and Q&A-style articles to help readers understand complex topics like the Covid-19 vaccines. To enhance this style of journalism, we experimented with large language models to match questions to answers, even if the reader asks their question in a novel way.

Last year we launched a new research effort to explore generating open-ended questions for news articles. Our hypothesis is that understanding the questions our news articles are implicitly answering may be helpful in the reporting process and may ultimately enable us to create FAQ and Q&A-style articles more efficiently.

You can find more information here: https://rd.nytimes.com/projects/generating-open-ended-questions-from-news-articles

This was fundamentally different from what I have been doing because I had to work towards upholding journalism values such as accuracy and verifiability. In creativity, your model can generate something that does not require attribution. But, when working on a project that deals with news and journalism, the focus is on factuality.

Q: One of your five research papers at EMNLP was from your time at the NY Times, right? 

Recent work on question generation has primarily focused on factoid questions such as who, what, where, and when about basic facts. Generating open-ended why, how, what, etc., questions that require long-form answers has proven more difficult. To facilitate the generation of open-ended questions, we propose CONSISTENT, a new end-to-end system for generating open-ended questions that are answerable from and faithful to the input text. Using news articles as a trustworthy foundation for experimentation, we demonstrate our model’s strength over several baselines using both automatic and human-based evaluations. We contribute an evaluation dataset of expert-generated open-ended questions and discuss potential downstream applications for news media organizations.


Q: What are you working on now? What are the kinds of research questions that you hope to answer?

Much of my recent and upcoming work is on human-AI collaboration for creativity. I recently worked on developing methods and evaluation frameworks for two creative tasks–poetry generation and visual metaphor generation–by leveraging collaboration between expert humans and state-of-the-art generative models. I further highlighted how collaboration improves the final output over either standalone models or only humans.

I have long focused on developing and evaluating machine learning models aimed at creativity in an isolated setting. This somehow limits their capacity to behave in an interactive setting with real humans. In a creative setting, it is crucial for models to understand human needs and provide assistance to augment human capabilities and improve performance based on human edits or feedback over time. So that is my focus now.

Q: About doing a PhD, what are the things you wished you knew before starting it?

This is a difficult question. Pursuing a PhD can be a really fun experience, but at the same time, it can be daunting. There is a lot of uncertainty around research questions and whether something will work or not. I wish I had been a little easier on myself and not taken everything personally. Like, if an idea didn’t work, instead of spending months trying to make it work, it is okay to give up and move in a different direction.

Q: What are your tips for people who want to pursue a PhD?

One of the things I learned during my PhD is to focus on what you care about. There are hundreds of researchers who might work on slightly dense areas, while your work can feel niche. This is not a problem. When I started working on NLP and creativity, the field still felt very young, but over the past three to four years, it has grown tremendously.

Your advisor will be one of the most important people in your PhD. It is essential to have good communication and working chemistry with them. One of the reasons my PhD felt like so much fun is because my advisor and I cared about the same problems.

Form a community and foster friendships with your lab mates, talk about research, or email a colleague whose work moved you and get a coffee with them at a conference. Also, try for opportunities to work with people in your lab or your community. It helps us learn so much.

11 Papers Accepted to NeurIPS 2022

Researchers from the department presented machine learning and artificial intelligence research at the thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2022).


Finding and Listing Front-door Adjustment Sets
Hyunchai Jeong Purdue University, Jin Tian Iowa State University, Elias Bareinboim Columbia University

Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such effects is Pearl’s front-door (FD) criterion. The definition of the FD criterion is declarative, only allowing one to decide whether a specific set satisfies the criterion. In this paper, we present algorithms for finding and enumerating possible sets satisfying the FD criterion in a given causal diagram. These results are useful in facilitating the practical applications of the FD criterion for causal effects estimation and helping scientists to select estimands with desired properties, e.g., based on cost, feasibility of measurement, or statistical power.


Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness
Amin Jaber Purdue University, Adele Ribeiro Columbia University, Jiji Zhang Hong Kong Baptist University, Elias Bareinboim Columbia University

One common task in many data sciences applications is to answer questions about the effect of new interventions, like: `what would happen to Y if we make X equal to x while observing covariates Z=z?’. Formally, this is known as conditional effect identification, where the goal is to determine whether a post-interventional distribution is computable from the combination of an observational distribution and assumptions about the underlying domain represented by a causal diagram. A plethora of methods was developed for solving this problem, including the celebrated do-calculus [Pearl, 1995]. In practice, these results are not always applicable since they require a fully specified causal diagram as input, which is usually not available. In this paper, we assume as the input of the task a less informative structure known as a partial ancestral graph (PAG), which represents a Markov equivalence class of causal diagrams, learnable from observational data. We make the following contributions under this relaxed setting. First, we introduce a new causal calculus, which subsumes the current state-of-the-art, PAG-calculus. Second, we develop an algorithm for conditional effect identification given a PAG and prove it to be both sound and complete. In words, failure of the algorithm to identify a certain effect implies that this effect is not identifiable by any method. Third, we prove the proposed calculus to be complete for the same task.


Online Reinforcement Learning for Mixed Policy Scopes
Junzhe Zhang Columbia University, Elias Bareinboim Columbia University

Combination therapy refers to the use of multiple treatments — such as surgery, medication, and behavioral therapy – to cure a single disease, and has become a cornerstone for treating various conditions including cancer, HIV, and depression. All possible combinations of treatments lead to a collection of treatment regimens (i.e., policies) with mixed scopes, or what physicians could observe and which actions they should take depending on the context. In this paper, we investigate the online reinforcement learning setting for optimizing the policy space with mixed scopes. In particular, we develop novel online algorithms that achieve sublinear regret compared to an optimal agent deployed in the environment. The regret bound has a dependency on the maximal cardinality of the induced state-action space associated with mixed scopes. We further introduce a canonical representation for an arbitrary subset of interventional distributions given a causal diagram, which leads to a non-trivial, minimal representation of the model parameters.


Masked Prediction: A Parameter Identifiability View
Bingbin Liu Carnegie Mellon University, Daniel Hsu Columbia University, Pradeep Ravikumar Carnegie Mellon University, Andrej Risteski Carnegie Mellon University

The vast majority of work in self-supervised learning have focused on assessing recovered features by a chosen set of downstream tasks. While there are several commonly used benchmark datasets, this lens of feature learning requires assumptions on the downstream tasks which are not inherent to the data distribution itself. In this paper, we present an alternative lens, one of parameter identifiability: assuming data comes from a parametric probabilistic model, we train a self-supervised learning predictor with a suitable parametric form, and ask whether the parameters of the optimal predictor can be used to extract the parameters of the ground truth generative model.Specifically, we focus on latent-variable models capturing sequential structures, namely Hidden Markov Models with both discrete and conditionally Gaussian observations. We focus on masked prediction as the self-supervised learning task and study the optimal masked predictor. We show that parameter identifiability is governed by the task difficulty, which is determined by the choice of data model and the amount of tokens to predict. Technique-wise, we uncover close connections with the uniqueness of tensor rank decompositions, a widely used tool in studying identifiability through the lens of the method of moments.


Learning single-index models with shallow neural networks
Alberto Bietti Meta AI/New York University, Joan Bruna New York University, Clayton Sanford Columbia University, Min Jae Song New York University

Single-index models are a class of functions given by an unknown univariate link” function applied to an unknown one-dimensional projection of the input. These models are particularly relevant in high dimension, when the data might present low-dimensional structure that learning algorithms should adapt to. While several statistical aspects of this model, such as the sample complexity of recovering the relevant (one-dimensional) subspace, are well-understood, they rely on tailored algorithms that exploit the specific structure of the target function. In this work, we introduce a natural class of shallow neural networks and study its ability to learn single-index models via gradient flow. More precisely, we consider shallow networks in which biases of the neurons are frozen at random initialization. We show that the corresponding optimization landscape is benign, which in turn leads to generalization guarantees that match the near-optimal sample complexity of dedicated semi-parametric methods.

On Scrambling Phenomena for Randomly Initialized Recurrent Networks
Evangelos Chatziafratis University of California Santa Cruz, Ioannis Panageas University of California Irvine, Clayton Sanford Columbia University, Stelios Stavroulakis University of California Irvine

Recurrent Neural Networks (RNNs) frequently exhibit complicated dynamics, and their sensitivity to the initialization process often renders them notoriously hard to train. Recent works have shed light on such phenomena analyzing when exploding or vanishing gradients may occur, either of which is detrimental for training dynamics. In this paper, we point to a formal connection between RNNs and chaotic dynamical systems and prove a qualitatively stronger phenomenon about RNNs than what exploding gradients seem to suggest. Our main result proves that under standard initialization (e.g., He, Xavier etc.), RNNs will exhibit \textit{Li-Yorke chaos} with \textit{constant} probability \textit{independent} of the network’s width. This explains the experimentally observed phenomenon of \textit{scrambling}, under which trajectories of nearby points may appear to be arbitrarily close during some timesteps, yet will be far away in future timesteps. In stark contrast to their feedforward counterparts, we show that chaotic behavior in RNNs is preserved under small perturbations and that their expressive power remains exponential in the number of feedback iterations. Our technical arguments rely on viewing RNNs as random walks under non-linear activations, and studying the existence of certain types of higher-order fixed points called \textit{periodic points} in order to establish phase transitions from order to chaos.


Patching open-vocabulary models by interpolating weights
Gabriel Ilharco University of Washington, Mitchell Wortsman University of Washington, Samir Yitzhak Gadre Columbia University, Shuran Song Columbia University, Hannaneh Hajishirzi University of Washington, Simon Kornblith Google Brain, Ali Farhadi University of Washington, Ludwig Schmidt University of Washington

Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. However, there are still settings where their zero-shot performance is far from optimal. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Towards this goal, we introduce PAINT, a patching method that uses interpolations between the weights of a model before fine-tuning and the weights after fine-tuning on a task to be patched. On nine tasks where zero-shot CLIP performs poorly, PAINT increases accuracy by 15 to 60 percentage points while preserving accuracy on ImageNet within one percentage point of the zero-shot model. PAINT also allows a single model to be patched on multiple tasks and improves with model scale. Furthermore, we identify cases of broad transfer, where patching on one task increases accuracy on other tasks even when the tasks have disjoint classes. Finally, we investigate applications beyond common benchmarks such as counting or reducing the impact of typographic attacks on CLIP. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch.


ASPiRe: Adaptive Skill Priors for Reinforcement Learning
Mengda Xu Columbia University, Manuela Veloso JP Morgan/Carnegie Mellon University, Shuran Song Columbia University

We introduce ASPiRe (Adaptive Skill Prior for RL), a new approach that leverages prior experience to accelerate reinforcement learning. Unlike existing methods that learn a single skill prior from a large and diverse dataset, our framework learns a library of different distinction skill priors (i.e., behavior priors) from a collection of specialized datasets, and learns how to combine them to solve a new task. This formulation allows the algorithm to acquire a set of specialized skill priors that are more reusable for downstream tasks; however, it also brings up additional challenges of how to effectively combine these unstructured sets of skill priors to form a new prior for new tasks. Specifically, it requires the agent not only to identify which skill prior(s) to use but also how to combine them (either sequentially or concurrently) to form a new prior. To achieve this goal, ASPiRe includes Adaptive Weight Module (AWM) that learns to infer an adaptive weight assignment between different skill priors and uses them to guide policy learning for downstream tasks via weighted Kullback-Leibler divergences. Our experiments demonstrate that ASPiRe can significantly accelerate the learning of new downstream tasks in the presence of multiple priors and show improvement on competitive baselines.


Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Zhenhailong Wang Columbia University, Manling Li Columbia University, Ruochen Xu Microsoft, Luowei Zhou Meta, Jie Lei Meta, Xudong Lin Columbia University, Shuohang Wang Microsoft, Ziyi Yang Stanford University, Chenguang Zhu Stanford University, Derek Hoiem University of Illinois, Shih-Fu Chang Columbia University, Mohit Bansal University of North Carolina Chapel Hill, Heng Ji University of Illinois

The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-to-text decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack the ability to generate text for unseen tasks in a few-shot setting. We propose VidIL, a few-shot Video-language Learner via Image and Language models, which demonstrates strong performance on few-shot video-to-text tasks without the necessity of pretraining or finetuning on any video datasets. We use image-language models to translate the video content into frame captions, object, attribute, and event phrases, and compose them into a temporal-aware template. We then instruct a language model, with a prompt containing a few in-context examples, to generate a target output from the composed content. The flexibility of prompting allows the model to capture any form of text input, such as automatic speech recognition (ASR) transcripts. Our experiments demonstrate the power of language models in understanding videos on a wide variety of video-language tasks, including video captioning, video question answering, video caption retrieval, and video future event prediction. Especially, on video future event prediction, our few-shot model significantly outperforms state-of-the-art supervised models trained on large-scale video datasets.Code and processed data are publicly available for research purposes at https://github.com/MikeWangWZHL/VidIL.


Implications of Model Indeterminacy for Explanations of Automated Decisions
Marc-Etienne Brunet University of Toronto, Ashton Anderson University of Toronto, Richard Zemel Columbia University

There has been a significant research effort focused on explaining predictive models, for example through post-hoc explainability and recourse methods. Most of the proposed techniques operate upon a single, fixed, predictive model. However, it is well-known that given a dataset and a predictive task, there may be a multiplicity of models that solve the problem (nearly) equally well. In this work, we investigate the implications of this kind of model indeterminacy on the post-hoc explanations of predictive models. We show how it can lead to explanatory multiplicity, and we explore the underlying drivers. We show how predictive multiplicity, and the related concept of epistemic uncertainty, are not reliable indicators of explanatory multiplicity. We further illustrate how a set of models showing very similar aggregate performance on a test dataset may show large variations in their local explanations, i.e., for a specific input. We explore these effects for Shapley value based explanations on three risk assessment datasets. Our results indicate that model indeterminacy may have a substantial impact on explanations in practice, leading to inconsistent and even contradicting explanations.


Reconsidering Deep Ensembles
Taiga Abe Columbia University, Estefany Kelly Buchanan Columbia University, Geoff Pleiss Columbia University, Richard Zemel Columbia University, John Cunningham Columbia University

Ensembling neural networks is an effective way to increase accuracy, and can often match the performance of individual larger models. This observation poses a natural question: given the choice between a deep ensemble and a single neural network with similar accuracy, is one preferable over the other? Recent work suggests that deep ensembles may offer distinct benefits beyond predictive power: namely, uncertainty quantification and robustness to dataset shift. In this work, we demonstrate limitations to these purported benefits, and show that a single (but larger) neural network can replicate these qualities. First, we show that ensemble diversity, by any metric, does not meaningfully contribute to an ensemble’s ability to detect out-of-distribution (OOD) data, but is instead highly correlated with the relative improvement of a single larger model. Second, we show that the OOD performance afforded by ensembles is strongly determined by their in-distribution (InD) performance, and – in this sense – is not indicative of any “effective robustness.” While deep ensembles are a practical way to achieve improvements to predictive power, uncertainty quantification, and robustness, our results show that these improvements can be replicated by a (larger) single model

The Undergraduate Computer and Data Science Research Fair

The inaugural fair gave students the opportunity to take their research from paper to presentation and showcase their hard work. 


The projects of the Undergraduate Computer and Data Science Research Fair fell under the themes of Data Science and Society, Interdisciplinary Data Science Applications, Data Science and Computer Science Research. Posters and demonstrations were proudly hosted by twenty-five students from SEAS, Barnard College, Columbia College, and the School of General Studies. 

CS Chair Luca Carloni and Clifford Stein, Interim Director of the Data Science Institute, issued the four awards, including best overall and best in each of the three tracks. 
CS & Data Science Research
Improving Model Training via Self-learned Label Representations 
Xiao Yu, Senior, SEAS
Interdisciplinary Data Science
Outlier Splicing Analysis of Autism Spectrum Disorder using RNA-seq Data
Sophia Sowinski, Junior, Barnard College
Data Science & Society
Legal and Political Stance Detection of SCOTUS Language 
Noah Bergam, Sophomore, Columbia College
Machine Learning assisted Age-Related Macular Degeneration Detection using Indirect Fundoscopy and iOS Device
Manan Vij, Freshman, Columbia College

The fair was organized by the Data, Media and Society Center at the Data Science Institute, Columbia University; the Barnard Program in Computer Science and the Vagelos Computational Science Center; and the Department of Computer Science at Columbia Engineering led by Eugene Wu, Susan McGregor, Rebecca Wright, and Alexis Avedisian.


Voices of CS: Cheng Chi

Second-year PhD student Cheng Chi talks about how his research on robotic control won a Best Paper Award at RSS 2022


In the Columbia Artificial Intelligence and Robotics (CAIR) Lab, Cheng Chi stands in front of a robotic arm. At the end of the arm sits a yellow plastic cup. His goal at the moment is to use a piece of rope to hit the cup to the ground.

“I never thought I would have to do this as part of a research project,” said Chi, a second-year PhD student. He was conducting the exercise to gain a better understanding of physical movement and how it can be applied to a robotic control system.

Existing robotic systems struggle to precisely manipulate objects with complex dynamics, such as hitting a small target with a whip or swinging tablecloths to an exact location. While these tasks are quite hard even for humans, we usually have a good intuition about how to change our actions after a failed attempt, and iteratively get closer to the goal.

Cheng Chi in the CAIR Lab

Chi was able to knock the cup off after five tries. Now, it’s the robot’s turn to fling the piece of rope. It takes the robot four times to hit the target during the experiment (in general less than 10 times). The algorithm/neural network was trained in a simulator using a large amount of data. The robot, called Oolong, had to hit a target and was tested on different kinds of ropes it had never seen before.

Together with Assistant Professor Shuran Song and colleagues from the CAIR Lab, Chi worked to formalize this intuition into an algorithm called Iterative Residual Policy (IRP), a general learning framework for repeatable tasks with complex dynamics where a single model was trained using inaccurate simulation data. IRP can learn from that data and hit many targets with unfamiliar ropes in real robotic experiments, reaching sub-inch accuracy, and demonstrating its strong generalization capability.

This research brings robots from factories, where everything is rigid and can be accurately modeled, closer to everyday households filled with dirty laundry, raw vegetables to be washed in the sink, and leftover food to be cleaned from the fridge. It could potentially alleviate the labor shortage in food, retail, and logistics due to the aging population in many parts of the world. This could also enable the automation of simple tasks like changing bed sheets and badges in hospitals with infectious disease patients.

The team won a Best Paper Award at the Robot Science and Systems Conference (RSS 2022). We caught up with Chi to find out more about his research and PhD life at Columbia.

Q: How did you become part of the research project?

This is part of a grant from the Toyota Research Institute on deformable object manipulation. For this specific project, I wanted to explore more complex and dynamic forms of robotic manipulation and control. As the primary researcher of this project, I decided on the research topic, problem, and task.


Q: How long did you work on the project? What did you have to do, or read to prepare to make the system?

The project started in May 2021. I did a lot of research about control theory for underactuated systems, chaos, and how to work with robot hardware.

Classical robotics literature divides the operation of a robot into three stages — perception, planning, and control. In my previous research, I studied perception and the planning stage of robotics. However, I realized that my knowledge still has a noticeable hole in control theory and systems that control the function and movement of robots.

I believe that I will never become a full-fledged roboticist without understanding all parts of robotic operations. Therefore, I intentionally steered this project toward control which allows me to read more into control-related literature and classes.

For example, I went over the YouTube recording of MIT’s underactuated robotics, taught by Professor Russ Tedrake, who has been known for his contributions to the control of locomotion systems (such as Boston Dynamic’s quadruped robots).

Another interesting thing about control is that, unlike planning, the control of the human body mostly happens at a subconscious level. Therefore, understanding more about control also gave me more insight into how the human body works.

The key realization came after months of reflecting on how I achieved certain tasks and how to formulate such a problem.

Since the relatively early stage of this project (after I decided to tackle the rope whipping task), I had this lingering feeling that being able to adjust the next action based on the error of my previous action is critical for how humans accomplish this task (by observing myself doing it). But I wasn’t able to connect it with math and concrete algorithms.

The next few months were spent playing with data collected in simulations to understand the structure of this task and problem. I often spent a few afternoons a week just staring at my iPad notes, sketching potential algorithms that can solve this task efficiently. Most of them were futile. However, one afternoon in late September, I suddenly came up with the idea that connects my lingering feeling to this concrete algorithm. And the rest was mostly planning out experiments, executing, and verifying results.


Q: Why did you decide to do research on robotic control?

I decided on the research project jointly on what is missing in the field and what I wanted to learn. For example, I wanted to get into control last summer, so I took classes online and read relevant papers to build a foundation. I noticed that the missing piece in the field is deformable manipulation with precision.

Existing robotic algorithms often assume the object being manipulated is rigid, and ignore its physics/dynamics, due to its complexity. My research thrust has been targeting this complexity (of object physics and non-rigidity) head-on, which hopefully will result in better algorithms that will improve the overall performance and robustness of robotics systems, outside of confined/structured industrial environments.

Whipping a piece of rope is one of the simplest instances of dynamic deformable object manipulation, without the additional perceptive complexity such as self-occlusion, etc. However, we believe that whipping a piece of rope and tablecloth is representative of the class of problem we are interested in and that there is no existing robotic system/algorithm that can accomplish this task. Therefore, our algorithm has expanded what is considered possible in robotics.

I thought that it would be cool to simplify it to a minimum-working task, like whipping. Whipping a piece of rope or cloth accurately requires adapting existing skills which humans are good at but it is very difficult for robots to do.

Humans can hit targets with reasonable accuracy after usually 10~20 trials. The best algorithm before IRP takes 100-1000+ trials to get there.

The project spanned 10 months and it was not easy, since solving this novel and challenging task requires going beyond the common paradigm in the field, for example, reinforcement learning or system identification.

I tried three ideas at first and none of them worked or advanced the field to a satisfying degree for me. The final idea was inspired by some studies from the biomechanics/neural science community that I came across while doing research.

While I was struggling with this project, my advisor pointed me to this recording of an RSS 2020 workshop. I was fascinated by one of the talks by Professor Dagmar Stenard and her findings from the biomechanical perspective of how humans minimize uncertainty and avoid the chaotic region of the state space when taking actions.

I read further into her publications and was pleasantly surprised that her group was studying the same rope-whipping problem. Their algorithm was crude and they only tested in simulation with many additional assumptions, but I really liked their problem formulation of the whipping task and their use of action primitive, which dramatically reduced the number of parameters needed to describe the dynamic and continuous robot action.

They also demonstrated that their action primitives (that bio literature believes humans also use) are sufficient for this task. Therefore, I took their problem formulation and tweaked their action primitive to better fit real robotic hardware, and eventually developed the IRP algorithm on top of that.

Q: Why did you decide to use different kinds of ropes for the project?

The type of rope we simulated for training is modeled after a thick cotton rope we bought on Amazon. However, due to the various complex physical properties and their effects, the rope modeled in simulation behaves significantly differently from its real-world counterpart. This is an instance of a well-known challenge in the robotics community called “sim2real gap”.

Since the deep-learning revolution (~2014), a large body of robotic algorithms emerged that have shown very promising results in simulated environments. However, they also rely on a large amount of data for training (our algorithm included), which is only feasible to collect in simulation. If the behavior of objects in simulation matches exactly their counterparts in real life, in theory, we can directly apply these data-hungry algorithms to the real world. Unfortunately, this is far from the truth, and the difference is especially big for deformable objects.

The biggest contribution of this paper is providing a solution to close this “sim2real gap” for a limited class of problems (where the actions are repeatable, and the objects can be reset to the original state), i.e. the algorithm behaves just as good in the real world as in simulation, despite the simulation it was trained on is very “wrong”.

To further demonstrate how “wrong” the simulation can be while the algorithm still works, we cut out a long strip of cloth, that behaves like a gymnastic ribbon and treated it as the rope. We also bought a very thick leather bullwhip, that has a non-uniform density (it becomes thinner and thinner as it goes toward the tip), while all ropes we trained in simulation have uniform thickness and density. The experimental results on these two “ropes” were just as good.

Q: What do you think is the most interesting thing about doing research?

I like how researchers are able to try high-risk ideas that actually advance the field and also learn fundamental knowledge about the field. Working in industry usually constrains research options to low-risk ideas, while the engineering effort might be larger.


Q: How did your previous experiences prepare you for a PhD?

I gained my initial research experience during my undergrad at the University of Michigan, working on deformable object perception. I had multiple internships, as well as full-time jobs at autonomous vehicle companies, which taught me how to properly engineer a robotics software system.


Q: Why did you apply to Columbia and how was that process?

I applied to Columbia to work with Assistant Professor Shuran Song. Just before I graduated from undergrad, Shuran did a job talk at the University of Michigan. My undergrad research advisor Professor Dmitry Berenson was at her talk and he was really impressed. Berenson strongly recommended that I apply to work with her and he thought we would be a great fit. After researching her past publications, I did find a large overlap in our research interests and I only heard good words about her after asking other people who have worked with her.

At the time, I wasn’t really sure about getting a PhD, and because of the time needed to complete the applications, I only applied to two schools. The application website could have been improved, but the overall process is surprisingly smooth. I really like the idea that students are admitted by and to individual professors, and the professors make the decision.


Q: What has been the highlight of your time at Columbia?

The highlight of my time is being able to be taught and guided by my advisor, as well as other PhD students.


Q: You are starting the third year of your PhD at Columbia, do you think your skills have improved? In which ways?

I think what improved the most was to think more structurally and not be buried by the details. Due to the engineering complexity of robotic systems, there are thousands of variables and decisions, large and small, I needed to make for the project to progress. For example, on the high level, how to model the rope in the simulation, how to model the robot, how to represent the observation and actions, how the model should be architected, etc.

For an inexperienced researcher like myself, it is not obvious which one of these parameters will make or break the project, or will only yield a small change in the final performance. So, I over-analyzed, over-engineered, and over-thought the small problems. Fortunately, Shuran often called out that some of these decisions probably don’t matter that much, and choosing an arbitrary path to go forward is strictly better than spending time thinking about which one is better.

The problem is that this is mostly based on intuition. Shuran can’t always give evidence of why one thing doesn’t matter and why another does. But fortunately, I think I am getting a better grasp of these intuitions. It will become easier for me as time passes and I become an expert in robotics.

I also have found that it is really important to communicate clearly, both in meetings and when writing things down for reports or even emails. Learning by example from my advisor also helps a lot.


Q: What is your advice to students on how to navigate their time at Columbia? If they want to do research what should they know or do to prepare?

New students going into research should try as hard as possible to push through the first research project. It is always hard in the beginning, and it might feel impossible, but you can do it. Build up a tolerance for failure and continue to try different things, which is often critical to making a contribution to the field.



5 Research Papers Accepted to ICML 2022

Papers from CS researchers have been accepted to the 38th International Conference on Machine Learning (ICML 2021)

Below are the abstracts and links to the accepted papers.


Simple And Near-Optimal Algorithms For Hidden Stratification And Multi-Group Learning
Christopher Tosh Memorial Sloan Kettering Cancer Center, Daniel Hsu Columbia University

Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. This paper studies the structure of solutions to the multi-group learning problem and provides simple and near-optimal algorithms for the learning problem.


On Measuring Causal Contributions Aia Do-Interventions
Yonghan Jung Purdue University, Shiva Kasiviswanathan Amazon, Jin Tian Iowa State University, Dominik Janzing Amazon, Patrick Bloebaum Amazon, Elias Bareinboim Columbia University

Causal contributions measure the strengths of different causes to a target quantity. Understanding causal contributions is important in empirical sciences and data-driven disciplines since it allows to answer practical queries like “what are the contributions of each cause to the effect?” In this paper, we develop a principled method for quantifying causal contributions. First, we provide desiderata of properties (axioms) that causal contribution measures should satisfy and propose the do-Shapley values (inspired by do-interventions (Pearl, 2000)) as a unique method satisfying these properties. Next, we develop a criterion under which the do-Shapley values can be efficiently inferred from non-experimental data. Finally, we provide do-Shapley estimators exhibiting consistency, computational feasibility, and statistical robustness. Simulation results corroborate with the theory.


Partial Counterfactual Identification From Observational And Experimental Data
Junzhe Zhang Columbia University, Jin TianIowa Iowa State University, Elias Bareinboim Columbia University

This paper investigates the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram. We show that all counterfactual distributions in an arbitrary structural causal model (SCM) with discrete observed domains could be generated by a canonical family of SCMs with the same causal diagram where unobserved (exogenous) variables are also discrete, taking values in finite domains. Utilizing the canonical SCMs, we translate the problem of bounding counterfactuals into that of polynomial programming whose solution provides optimal bounds for the counterfactual query. Solving such polynomial programs is in general computationally expensive. We then develop effective Monte Carlo algorithms to approximate optimal bounds from a combination of observational and experimental data. Our algorithms are validated extensively on synthetic and real-world datasets.


Counterfactual Transportability: A Formal Approach
Juan D. Correa Universidad Autonoma de Manizales, Sanghack Lee Seoul National University, Elias Bareinboim Columbia University

Generalizing causal knowledge across environments is a common challenge shared across many of the data-driven disciplines, including AI and ML. Experiments are usually performed in one environment (e.g., in a lab, on Earth, in a training ground), almost invariably, with the intent of being used elsewhere (e.g., outside the lab, on Mars, in the real world), in an environment that is related but somewhat different than the original one, where certain conditions and mechanisms are likely to change. This generalization task has been studied in the causal inference literature under the rubric of transportability (Pearl and Bareinboim, 2011). While most transportability works focused on generalizing associational and interventional distributions, the generalization of counterfactual distributions has not been formally studied. In this paper, we investigate the transportability of counterfactuals from an arbitrary combination of observational and experimental distributions coming from disparate domains. Specifically, we introduce a sufficient and necessary graphical condition and develop an efficient, sound, and complete algorithm for transporting counterfactual quantities across domains in nonparametric settings. Failure of the algorithm implies the impossibility of generalizing the target counterfactual from the available data without further assumptions.


Variational Inference for Infinitely Deep Neural Networks
Achille Nazaret Columbia University, David Blei Columbia University

We introduce the unbounded depth neural network (UDN), an infinitely deep probabilistic model that adapts its complexity to the training data. The UDN contains an infinite sequence of hidden layers and places an unbounded prior on a truncation ℓ, the layer from which it produces its data. Given a dataset of observations, the posterior UDN provides a conditional distribution of both the parameters of the infinite neural network and its truncation. We develop a novel variational inference algorithm to approximate this posterior, optimizing a distribution of the neural network weights and of the truncation depth ℓ, and without any upper limit on ℓ. To this end, the variational family has a special structure: it models neural network weights of arbitrary depth, and it dynamically creates or removes free variational parameters as its distribution of the truncation is optimized. (Unlike heuristic approaches to model search, it is solely through gradient-based optimization that this algorithm explores the space of truncations.) We study the UDN on real and synthetic data. We find that the UDN adapts its posterior depth to the dataset complexity; it outperforms standard neural networks of similar computational complexity; and it outperforms other approaches to infinite-depth neural networks.