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

21 papers from CS researchers accepted to NeurIPS 2019

The 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) fosters the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. 

The annual meeting is one of the premier gatherings in artificial intelligence and machine learning that featured talks, demos from industry partners as well as tutorials. Professor Vishal Misra, with colleagues from the Massachusetts Institute of Technology (MIT), held a tutorial on synthetic control.

At this year’s NeurIPS, 21 papers from the department were accepted to the conference. Computer science professors and students worked with researchers from the statistics department and the Data Science Institute.

Noise-tolerant Fair Classification
Alex Lamy Columbia University, Ziyuan Zhong Columbia University, Aditya Menon Google, Nakul Verma Columbia University

Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender) and is usually done under the assumption that the sensitive feature available in a training sample is perfectly reliable.

This assumption may be violated in many real-world cases: for example, respondents to a survey may choose to conceal or obfuscate their group identity out of fear of potential discrimination. In the paper, the researchers show that fair classifiers can still be used given noisy sensitive features by simply changing the desired fairness-tolerance. Their procedure is empirically effective on two relevant real-world case-studies involving sensitive feature censoring.

Poisson-randomized Gamma Dynamical Systems
Aaron Schein UMass Amherst, Scott Linderman Columbia University, Mingyuan Zhou University of Texas at Austin, David Blei Columbia University, Hanna Wallach MSR NYC

This paper presents a new class of state space models for count data. It derives new properties of the Poisson-randomized gamma distribution for efficient posterior inference.

Using Embeddings to Correct for Unobserved Confounding in Networks
Victor Veitch Columbia University, Yixin Wang Columbia University, David Blei Columbia University

This paper address causal inference in the presence of unobserved confounder when proxy is available for the confounders in the form of a network connecting the units. For example, the link structure of friendships in a social network reveals information about the latent preferences of people in that network. The researchers show how modern network embedding methods can be exploited to harness the network estimation for efficient causal adjustment.

Variational Bayes Under Model Misspecification
Yixin Wang Columbia University, David Blei Columbia University

The paper characterizes the theoretical properties of a popular machine learning algorithm, variational Bayes (VB). The researchers studied the VB under model misspecification, which is the setting that is most aligned with the practice, and show that the VB posterior is asymptotically normal and centers at the value that minimizes the Kullback-Leibler (KL) divergence to the true data-generating distribution. 

As a consequence, they found that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions. In other words, VB pays a negligible price in producing posterior predictive distributions. It explains the widely observed phenomenon that VB achieves comparable predictive accuracy with MCMC even though VB uses an approximating family.

Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
Emmanouil-Vasileios Vlatakis-Gkaragkounis Columbia University, Lampros Flokas Columbia University, Georgios Piliouras Singapore University of Technology and Design

The paper introduces a model that captures a min-max competition over complex error landscapes and shows that even a simplified model can provably replicate some of the most commonly reported failure modes of GANs (non-convergence, deadlock in suboptimal states, etc).

Moreover, the researchers were able to understand the hidden structure in these systems — the min-max competition can lead to system behavior that is similar to that of energy preserving systems in physics (e.g. connected pendulums, many-body problems, etc). This makes it easier to understand why these systems can fail and gives new tools in the design of algorithms for training GANs. 

Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
Junzhe Zhang Columbia University, Elias Bareinboim Columbia University

Dynamic Treatment Regimes (DTRs) are particularly effective for managing chronic disorders and is arguably one of the key aspects towards more personalized decision-making. The researchers developed the first adaptive algorithm that achieves near-optimal regret in DTRs in online settings, while leveraging the abundant, yet imperfect confounded observations. Applications are given to personalized medicine and treatment recommendation in clinical decision support.

Paraphrase Generation with Latent Bag of Words
Yao Fu Columbia University, Yansong Feng Peking University, John Cunningham University of Columbia

The paper proposes a latent bag of words model for differentiable content planning and surface realization in text generation. This model generates paraphrases with clear steps, adding interpretability and controllability of existing neural text generation models. 

Adapting Neural Networks for the Estimation of Treatment Effects
Claudia Shi Columbia University, David Blei Columbia University, Victor Veitch Columbia University

This paper addresses how to design neural networks to get very accurate estimates of causal effects from observational data. The researchers propose two methods based on insights from the statistical literature on the estimation of treatment effects. 

The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties “out-of-the-box”. Studies on benchmark datasets for causal inference show these adaptations outperform existing methods.

Efficiently Avoiding Saddle Points with Zero Order Methods: No Gradients Required
Emmanouil-Vasileios Vlatakis-Gkaragkounis Columbia University, Lampros Flokas Columbia University, Georgios Piliouras Singapore University of Technology and Design

The researchers prove that properly tailored zero-order methods are as effective as their first-order counterparts. This analysis requires a combination of tools from optimization theory, probability theory and dynamical systems to show that even without perfect knowledge of the shape of the error landscape, effective optimization is possible.

Metric Learning for Adversarial Robustness
Chengzhi Mao Columbia University, Ziyuan Zhong Columbia University, Junfeng Yang Columbia University, Carl Vondrick Columbia University, Baishakhi Ray Columbia University

Deep networks are well-known to be fragile to adversarial attacks. The paper introduces a novel Triplet Loss Adversarial (TLA) regulation that is the first method that leverages metric learning to improve the robustness of deep networks. This method is inspired by the evidence that deep networks suffer from distorted feature space under adversarial attacks. The method increases the model robustness and efficiency for the detection of adversarial attacks significantly.

Efficient Symmetric Norm Regression via Linear Sketching
Zhao Song University of Washington, Ruosong Wang Carnegie Mellon University, Lin Yang Johns Hopkins University, Hongyang Zhang TTIC, Peilin Zhong Columbia University

The paper studies linear regression problems with general symmetric norm loss and gives efficient algorithms for solving such linear regression problems via sketching techniques.

Rethinking Generative Coverage: A Pointwise Guaranteed Approach
Peilin Zhong Columbia University, Yuchen Mo Columbia University, Chang Xiao Columbia University, Pengyu Chen Columbia University, Changxi Zheng Columbia University

The paper presents a novel and  formal definition of mode coverage for generative models. It also gives a boosting algorithm to achieve this mode coverage guarantee.

How Many Variables Should Be Entered in a Principal Component Regression Equation?
Ji Xu Columbia University, Daniel Hsu Columbia University

The researchers studied the least-squares linear regression over $N$ uncorrelated Gaussian features that are selected in order of decreasing variance with the number of selected features $p$ can be either smaller or greater than the sample size $n$. And give an average-case analysis of the out-of-sample prediction error as $p,n,N \to \infty$ with $p/N \to \alpha$ and $n/N \to \beta$, for some constants $\alpha \in [0,1]$ and $\beta \in (0,1)$. In this average-case setting, the prediction error exhibits a “double descent” shape as a function of $p$. This also establishes conditions under which the minimum risk is achieved in the interpolating ($p>n$) regime.

Adaptive Influence Maximization with Myopic Feedback
Binghui Peng Columbia University, Wei Chen Microsoft Research

The paper investigates the adaptive influence maximization problem and provides upper and lower bounds for the adaptivity gaps under myopic feedback model. The results confirm a long standing open conjecture by Golovin and Krause (2011).

Towards a Zero-One Law for Column Subset Selection
Zhao Song University of Washington, David Woodruff Carnegie Mellon University, Peilin Zhong Columbia University

The researchers studied low-rank matrix approximation with general loss function and showed that if the loss function has several good properties, then there is an efficient way to compute a good low-rank approximation. Otherwise, it could be hard to compute a good low-rank approximation efficiently.

Average Case Column Subset Selection for Entrywise l1-Norm Loss
Zhao Song University of Washington, David Woodruff Carnegie Mellon University, Peilin Zhong Columbia University

The researchers studied how to compute an l1-norm loss low-rank matrix approximation to a given matrix. And showed that if the given matrix can be decomposed into a low-rank matrix and a noise matrix with a mild distributional assumption, we can obtain a (1+eps) approximation to the optimal solution.

A New Distribution on the Simplex with Auto-Encoding Applications
Andrew Stirn Columbia University, Tony Jebara Spotify, David Knowles Columbia University

The researchers developed a surrogate distribution for the Dirichlet that offers explicit, tractable reparameterization, the ability to capture sparsity, and has barycentric symmetry properties (i.e. exchangeability) equivalent to the Dirichlet. Previous works have used the Kumaraswamy distribution in a stick-breaking process to create a non-exchangeable distribution on the simplex. The method was improved by restoring exchangeability and demonstrating that approximate exchangeability is efficiently achievable. Lastly, the method was showcased in a variety of VAE semi-supervised learning tasks.

Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran Google Brain, Keyon Vafa Columbia University, Kumar Agrawal Google AI Resident, Laurent Dinh Google Brain, Ben Poole Google Brain

While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. The researchers extend normalizing flows to discrete events, using a simple change-of-variables formula not requiring log-determinant-Jacobian computations. Empirically, they find that discrete flows obtain competitive performance with or outperform autoregressive baselines on various tasks, including addition, Potts models, and language models.

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Murat Kocaoglu MIT-IBM Watson AI Lab IBM Research, Amin Jaber Purdue University, Karthikeyan Shanmugam MIT-IBM Watson AI Lab IBM Research NY, Elias Bareinboim Columbia University

This work is all about learning causal relationships – the classic aim of which is to characterize all possible sets that could produce the observed data. In the paper, the researchers provide a complete characterization of all possible causal graphs with observational and interventional data involving so-called ‘soft interventions’ on variables when the targets of soft interventions are known.

This work potentially could lead to discovery of other novel learning algorithms that are both sound and complete.

Identification of Conditional Causal Effects Under Markov Equivalence
Amin Jaber Purdue University, Jiji Zhang Lingnan University, Elias Bareinboim Columbia University

Causal identification is the problem of deciding whether a causal distribution is computable from a combination of qualitative knowledge about the underlying data-generating process, which is usually encoded in the form of a causal graph, and an observational distribution. Despite the obvious need for identifying causal effects throughout the data-driven sciences, in practice, finding the causal graph is a notoriously challenging task.

In this work, the researchers provide a relaxation of the requirement of having to specify the causal graph (based on substantive knowledge) and allow the input of the inference to be an equivalence class of causal graphs, which can be inferred from data. Specifically, they propose the first general algorithm to learn conditional causal effects entirely from data. This result is particularly useful for evaluating the impact of conditional plans and stochastic policies, which appear both in AI (in the context of reinforcement learning) and in the data-driven sciences.

Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Daniel Kumor Purdue University, Bryant Chen Brex Inc., Elias Bareinboim Columbia University

Regression analysis is one of the most common tools used in modern data science. While there is a great understanding and powerful technology to perform regression analysis in high dimensional spaces, the output of such a method is purely associational and devoid of any causal interpretation.

The researchers studied the problem of identification of structural (causal) coefficients in linear systems (deciding whether regression coefficients are amenable to causal interpretation, etc). Building on a technique called instrumental variables, they developed a new method called Instrumental Cutset, which partitions the systems into tractable components such that identification can be decided more efficiently. The resulting algorithm was efficient and strictly more powerful than the current state-of-the-art methods.