Richard Zemel - Columbia University

Brief Bio

I am a professor of computer science at Columbia. I am also the director of the NSF AI Institute for ARtificial and Natural Intelligence (ARNI).

I was previously the co-founder and inaugural research director of the Vector Institute for Artificial Intelligence, and I have been a visiting researcher at Amazon, Google, Meta and Spotify, and founded and ran a startup company, Smartfinance.

I am a Canadian Institute for Advanced Research AI Chair and on the Advisory Board of the Neural Information Processing Society. I received an AI Lifetime Achievement Award (CAIA) and a Pioneer of AI Award (NVIDIA).

Research & Teaching

By developing learning algorithms that flexibly adapt across tasks and environments, our research aims to create AI systems that are reliable, controllable, and trustworthy. Zgroup investigates how machine learning models can integrate diverse modalities, continually acquire new skills, quantify their own uncertainty, and remain robust in unfamiliar settings. Other interests include algorithmic fairness, interpretability, computational neuroscience, and applications of machine learning to high-stakes scientific and societal decisions.

I typically recruit one or two PhD students to join Zgroup each year. Prospective PhD students should apply to the PhD program. Due to the volume of email we receive, we unfortunately cannot respond to emails about applications.

In Fall 2026 I will teach two courses: Neural Networks & Deep Learning (COMS 4776), and Continual Learning & Memory Models (COMS 6998).

Current Zgroup PhD Students and Postdocs

Amogh Inamdar

Arjun Mani

Ben Eyre

Conlan Olson

Siddhartha Datta

Sruthi Sudhakar

Todd Morrill

Tom Zollo

Graduated PhD Students and Former Postdocs

Zhun Deng (2025), Assistant Professor at University of North Carolina

Marc-Etienne Brunet (2024), Research Scientist at ServiceNow AI Research

Elliot Creager (2023), Assistant Professor at University of Waterloo

David Madras (2022), Research Scientist at Google DeepMind

Jackson Wang (2022), Postdoctoral Fellow at Stanford University

Renjie Liao (2021), Assistant Professor at University of British Columbia

James Lucas (2021), Research Scientist at NVIDIA

Mengye Ren (2021), Assistant Professor at NYU

Jake Snell (2021), Postdoc at Princeton University

Eleni Triantafillou (2021), Senior Research Scientist at Google DeepMind

Jorn Jacobsen (2020), Principal Research Scientist at Isomorphic Labs

Marc Law (2020), Senior Research Scientist at NVIDIA

Kevin Swersky (2020), Research Scientist at Google DeepMind

Will Grathwohl (2019), Research Scientist at DeepMind

Danny Tarlow (2019), Research Scientist at Google DeepMind

Ethan Fetaya (2018), Assistant Professor at Bar-Ilan University

Yujia Li (2018), Research Scientist at Google DeepMind

Jamie Ryan Kiros (2017), Entrepeneur at Latvia

James Martens (2016), Staff Research Scientist at Google DeepMind

Maks Volkovs (2014), Chief AI Scientist at TD

Laurent Charlin (2013), Associate Professor at University of Montreal

Rama Natarajan (2013)

Amit Gruber (2009), Research Scientist at IBM

Xuming He (2009), Associate Professor at ShanghaiTech University

Benjamin Marlin (2009), Professor at University of Massachusetts, Amherst

David Ross (2008), Engineering Manager at Google DeepMind

Arnold Binas (2005), Senior Engineering Manager at Google

Danny Roobaert (2005), Founder at Lemaitre Capital

Miguel Carreira-Perpinan (2004), Professor at University of California, Merced

Tanya Schmah (2004), Associate Professor at University of Ottawa

Sagan Bolliger (2003), Master of Counselling Psychology at Adler University

Stephen Cowen (2001), Associate Professor at University of Arizona

Representative Papers

The papers below are representative examples drawn from our main research directions. Some develop systems that flexibly span language, vision, and real-world reasoning. Others tackle how to acquire new skills efficiently and stably as the world changes. Another strand develops models that know what they know — and what they don't — and provides reliable performance guarantees. A final strand builds systems that handle scenarios beyond their training data, producing trustworthy responses to ambiguous inputs.

2026

Teaser for Few-Shot Design Optimization by Exploiting Auxiliary Information

Few-Shot Design Optimization by Exploiting Auxiliary Information
Arjun Mani, Carl Vondrick, Richard Zemel
Paper Project Page BibTeX

Teaser for Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning

Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning
Zhenwei Tang, Amogh Inamdar, Ashton Anderson, Richard Zemel
Paper BibTeX

Teaser for Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language

Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
Max S. Bennett, Thomas P. Zollo, Richard Zemel
Paper BibTeX

Teaser for Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation

Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation
Thomas Zollo, Jimmy Wang, Richard Zemel
Paper BibTeX

Teaser for Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions

Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions
Ruomeng Ding, Tianwei Gao, Thomas P. Zollo, Eitan Bachmat, Richard Zemel, Zhun Deng
Paper BibTeX

2025

Teaser for Adaptive Elicitation of Latent Information Using Natural Language

Adaptive Elicitation of Latent Information Using Natural Language
Jimmy Wang, Thomas Zollo, Richard Zemel, Hongseok Namkoong
Paper BibTeX

Teaser for Confidence Calibration in Vision-Language-Action Models

Confidence Calibration in Vision-Language-Action Models
Thomas P Zollo, Richard Zemel
Paper BibTeX

Teaser for Guiding LLM Decision-Making with Fairness Reward Models

Guiding LLM Decision-Making with Fairness Reward Models
Zara Hall, Melanie Subbiah, Thomas P Zollo, Kathleen McKeown, Richard Zemel
Paper BibTeX

Teaser for Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads

Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads
Todd Morrill, Aahlad Puli, Murad Megjhani, Soojin Park, Richard Zemel
Paper BibTeX

Teaser for QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions

QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions
Zhun Deng, Thomas P Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard Zemel
Paper BibTeX

Teaser for Replay Can Provably Increase Forgetting

Replay Can Provably Increase Forgetting
Yasaman Mahdaviyeh, James Lucas, Mengye Ren, Andreas S. Tolias, Richard Zemel, Toniann Pitassi
Paper BibTeX

Teaser for Schemex: Discovering Structural Abstractions from Examples

Schemex: Discovering Structural Abstractions from Examples
Sitong Wang, Samia Menon, Dingzeyu Li, Xiaojuan Ma, Richard Zemel, Lydia B. Chilton
Paper BibTeX

Teaser for Societal Alignment Frameworks Can Improve LLM Alignment

Societal Alignment Frameworks Can Improve LLM Alignment
Karolina Stańczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin Böttinger, Jeremy Barnes, Jason Stanley, Jessica Montgomery, Richard Zemel, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P. Lillicrap, Ana Marasović, Sylvie Delacroix, Gillian K. Hadfield, Siva Reddy
Paper BibTeX

Teaser for Test-Time Warmup for Multimodal Large Language Models

Test-Time Warmup for Multimodal Large Language Models
Nikita Rajaneesh, Thomas Zollo, Richard Zemel
Paper BibTeX

Teaser for Towards Effective Discrimination Testing for Generative AI

Towards Effective Discrimination Testing for Generative AI
Thomas P. Zollo, Nikita Rajaneesh, Richard Zemel, Talia B. Gillis, Emily Black
Paper BibTeX

Teaser for Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation

Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation
Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris
Paper BibTeX

2024

Teaser for Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification

Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification
Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris
Paper BibTeX

Teaser for Controlling the World by Sleight of Hand

Controlling the World by Sleight of Hand
Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick, Carl Vondrick, Richard Zemel
Paper BibTeX

Teaser for Improving Predictor Reliability with Selective Recalibration

Improving Predictor Reliability with Selective Recalibration
Thomas P. Zollo, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard Zemel
Paper BibTeX

Teaser for Integrating Present and Past in Unsupervised Continual Learning

Integrating Present and Past in Unsupervised Continual Learning
Yipeng Zhang, Laurent Charlin, Richard Zemel, Mengye Ren
Paper BibTeX

Teaser for Online Algorithmic Recourse by Collective Action

Online Algorithmic Recourse by Collective Action
Elliot Creager, Richard Zemel
Paper BibTeX

Teaser for Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift

Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard Zemel
Paper BibTeX

Partial Federated Learning
Tiantian Feng, Anil Ramakrishna, Jimit Majmudar, Charith Peris, Jixuan Wang, Clement Chung, Richard Zemel, Morteza Ziyadi, Rahul Gupta
Paper BibTeX

Teaser for Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions

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

Teaser for Toward Informal Language Processing: Knowledge of Slang in Large Language Models

Toward Informal Language Processing: Knowledge of Slang in Large Language Models
Zhewei Sun, Qian Hu, Rahul Gupta, Richard Zemel, Yang Xu
Paper BibTeX

Teaser for Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities

Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities
Sachit Menon, Richard Zemel, Carl Vondrick
Paper BibTeX

2023

Teaser for "I'm fully who I am": Towards centering transgender and non-binary voices to measure biases in open language generation

"I'm fully who I am": Towards centering transgender and non-binary voices to measure biases in open language generation
Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
Paper BibTeX

Teaser for Coordinated replay sample selection for continual federated learning

Coordinated replay sample selection for continual federated learning
Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta
Paper BibTeX

Teaser for Differentially private decoding in large language models

Differentially private decoding in large language models
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard Zemel
Paper BibTeX

Teaser for Distribution-free statistical dispersion control for societal applications

Distribution-free statistical dispersion control for societal applications
Zhun Deng, Thomas Zollo, Jake Snell, Toniann Pitassi, Richard Zemel
Paper BibTeX

Teaser for FLIRT: Feedback Loop In-context Red Teaming

FLIRT: Feedback Loop In-context Red Teaming
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
Paper BibTeX

Teaser for ICL Markup: Structuring in-context learning using soft-token tags

ICL Markup: Structuring in-context learning using soft-token tags
Marc-Etienne Brunet, Ashton Anderson, Richard Zemel
Paper BibTeX

Teaser for JAB: Joint Adversarial Prompting and Belief Augmentation

JAB: Joint Adversarial Prompting and Belief Augmentation
Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Jwala Dhamala, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
Paper BibTeX

Teaser for On the steerability of large language models toward data-driven personas

On the steerability of large language models toward data-driven personas
Junyi Li, Ninareh Mehrabi, Charith Peris, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
Paper BibTeX

Teaser for Prompt Risk Control: A flexible framework for bounding the probability of high-loss predictions

Prompt Risk Control: A flexible framework for bounding the probability of high-loss predictions
Thomas Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel
Paper BibTeX

Teaser for Resolving ambiguities in text-to-image generative models

Resolving ambiguities in text-to-image generative models
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
Paper BibTeX

Teaser for Semantically informed slang interpretation

Semantically informed slang interpretation
Zhewei Sun, Richard Zemel, Yang Xu
Paper BibTeX

Teaser for SurfsUp: Learning fluid simulation for novel surfaces

SurfsUp: Learning fluid simulation for novel surfaces
Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard Zemel
Paper BibTeX

Teaser for Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies

Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
Paper BibTeX

2022

Teaser for Amortized Causal Discovery: Learning to infer causal graphs from time-series data

Amortized Causal Discovery: Learning to infer causal graphs from time-series data
Sindy Lowe, David Madras, Richard Zemel, Max Welling
Paper BibTeX

Teaser for Correlation and generalization under correlation shifts

Correlation and generalization under correlation shifts
Christina Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kummerer, Richard Zemel, Matthias Bethge
Paper BibTeX

Teaser for Deep ensembles work, but are they necessary?

Deep ensembles work, but are they necessary?
Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, Richard Zemel, John Cunningham
Paper BibTeX

Teaser for Implications of model indeterminacy for explanations of automated decisions

Implications of model indeterminacy for explanations of automated decisions
Marc-Etienne Brunet, Ashton Anderson, Richard Zemel
Paper BibTeX

Teaser for Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic
António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard Zemel
Paper BibTeX

2021

Teaser for A computational framework for slang generation

A computational framework for slang generation
Zhewei Sun, Richard Zemel, Yang Xu
Paper BibTeX

Teaser for A PAC-Bayesian approach to generalization bounds for graph neural networks

A PAC-Bayesian approach to generalization bounds for graph neural networks
Renjie Liao, Raquel Urtasun, Richard Zemel
Paper BibTeX

Teaser for Bayesian few-shot classification with one-vs-each Polya-Gamma augmented Gaussian Processes

Bayesian few-shot classification with one-vs-each Polya-Gamma augmented Gaussian Processes
Jake Snell, Richard Zemel.
Paper BibTeX

Teaser for Directly training joint energy-based models for conditional synthesis and calibrated prediction of multi-attribute data

Directly training joint energy-based models for conditional synthesis and calibrated prediction of multi-attribute data
Jacob Kelly, Richard Zemel, Will Grathwohl
Paper BibTeX

Teaser for Environment inference for invariant learning

Environment inference for invariant learning
Elliot Creager, Jorn Jacobsen, Richard Zemel.
Paper BibTeX

Teaser for Fairness and robustness in invariant learning: A case study in toxicity classification

Fairness and robustness in invariant learning: A case study in toxicity classification
Robert Adragna, Elliot Creager, David Madras, Richard Zemel
Paper BibTeX

Teaser for Identifying and benchmarking natural out-of-context prediction problems

Identifying and benchmarking natural out-of-context prediction problems
David Madras, Richard Zemel
Paper BibTeX

Teaser for NP-DRAW: A non-parametric structured latent variable model for image generation

NP-DRAW: A non-parametric structured latent variable model for image generation
Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao
Paper BibTeX

Teaser for On monotonic linear interpolation of neural network parameters

On monotonic linear interpolation of neural network parameters
James Lucas, Juhan Bae, Michael Zhang, Stanislav Fort, Richard Zemel, Roger Grosse
Paper BibTeX

Teaser for Online Unsupervised Learning of Visual Representations and Categories

Online Unsupervised Learning of Visual Representations and Categories
Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel
Paper BibTeX

Teaser for SketchEmbedNet: Learning novel concepts by imitating drawings

SketchEmbedNet: Learning novel concepts by imitating drawings
Alex Wang, Mengye Ren, Richard Zemel
Paper BibTeX

Teaser for Theoretical bounds on estimation error for meta-learning

Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Kameni, Toni Pitassi, Richard Zemel
Paper BibTeX

Teaser for Universal template for few-shot dataset generalization

Universal template for few-shot dataset generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin
Paper BibTeX

Teaser for Variational Model Inversion Attacks

Variational Model Inversion Attacks
Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani
Paper BibTeX

Teaser for Wandering within a world: Online contextualized few-shot learning

Wandering within a world: Online contextualized few-shot learning
Mengye Ren, Michael Iuzzolino, Michael Mozer, Richard Zemel
Paper BibTeX

2020

Teaser for Causal modeling for fairness in dynamical systems

Causal modeling for fairness in dynamical systems
Elliot Creager, David Madras, Toni Pitassi, Richard Zemel
Paper BibTeX

Teaser for Cutting out the middle-man: Training and evaluating energy-based models

Cutting out the middle-man: Training and evaluating energy-based models
Will Grathwohl, Jackson Wang, Jorn Jacobsen, David Duvenaud, Richard Zemel
Paper BibTeX

Teaser for Optimizing long-term social welfare in recommender systems: A constrained matching approach

Optimizing long-term social welfare in recommender systems: A constrained matching approach
Martin Mladenov, Elliot Creager, O Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier
Paper BibTeX

Teaser for Probing Few-Shot Generalization with Attributes

Probing Few-Shot Generalization with Attributes
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel
Paper BibTeX

Teaser for Shortcut learning in deep neural networks

Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix Wichmann
Paper BibTeX

Teaser for Understanding the limitations of conditional generative models

Understanding the limitations of conditional generative models
Ethan Fetaya, Joern-Henrik Jacobsen, Will Grathwohl, Richard Zemel
Paper BibTeX

2019

Teaser for A divergence minimization perspective on imitation learning methods

A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shane Gu
Paper BibTeX

Teaser for Aggregated momentum: Stability through passive damping

Aggregated momentum: Stability through passive damping
James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse
Paper BibTeX

Teaser for Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang
Paper BibTeX

Teaser for Dimensionality reduction for representing the knowledge of probabilistic models

Dimensionality reduction for representing the knowledge of probabilistic models
Marc Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard Zemel
Paper BibTeX

Teaser for Efficient graph generation with graph recurrent attention networks

Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William Hamilton, David Duvenaud, Raquel Urtasun, Richard Zemel
Paper BibTeX

Teaser for Excessive invariance causes adversarial vulnerability

Excessive invariance causes adversarial vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge
Paper BibTeX

Teaser for Fairness through causal awareness: Learning causal latent-variable models for biased data.

Fairness through causal awareness: Learning causal latent-variable models for biased data.
David Madras, Elliot Creager, Toni Pitassi, Richard Zemel
Paper BibTeX

Teaser for Flexibly fair representation learning by disentanglement

Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel
Paper BibTeX

Teaser for High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks

High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks
Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Paper BibTeX

Teaser for Incremental few-shot learning with attention attractor networks

Incremental few-shot learning with attention attractor networks
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel
Paper BibTeX

Teaser for LanczosNet: Multi-scale deep graph convolutional networks

LanczosNet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel
Paper BibTeX

Teaser for Lorentzian distance learning for hyperbolic representations

Lorentzian distance learning for hyperbolic representations
Marc Law, Renjie Liao, Jake Snell, Richard Zemel
Paper BibTeX

Teaser for Understanding the origins of bias in word embedding

Understanding the origins of bias in word embedding
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel
Paper BibTeX

2018

Teaser for Adversarial distillation of Bayesian neural network posteriors

Adversarial distillation of Bayesian neural network posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel
Paper BibTeX

Teaser for Graph Partition Neural Networks for Semi-Supervised Classification

Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel
Paper BibTeX

Teaser for Inference in Probabilistic Graphical Models by Graph Neural Networks

Inference in Probabilistic Graphical Models by Graph Neural Networks
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow
Paper BibTeX

Teaser for Learning adversarially fair and transferable representations

Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel
Paper BibTeX

Teaser for Learning latent subspaces in variational autoencoders

Learning latent subspaces in variational autoencoders
Jack Klys, Jake Snell, Richard Zemel
Paper BibTeX

Teaser for Meta-Learning for Semi-Supervised Few-Shot Classification

Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Paper BibTeX

Teaser for Neural guided constraint logic programming for program synthesis

Neural guided constraint logic programming for program synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel.
Paper BibTeX

Teaser for Neural relational inference for interacting systems

Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel
Paper BibTeX

Teaser for Predict responsibly: improving fairness and accuracy by learning to defer

Predict responsibly: improving fairness and accuracy by learning to defer
David Madras, Toni Pitassi, Richard Zemel
Paper BibTeX

Teaser for Reviving and improving recurrent back-propagation

Reviving and improving recurrent back-propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Zachary Pitkow, Raquel Urtasun, Richard Zemel
Paper BibTeX

The elephant in the room
Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Paper BibTeX

2017

Teaser for Causal effect inference with deep latent-variable models

Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling
Paper BibTeX

Teaser for Deep spectral clustering learning

Deep spectral clustering learning
Marc Law, Raquel Urtasun, Richard Zemel
Paper BibTeX

Dualing GANs
Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel
Paper BibTeX

Teaser for Efficient multiple instance metric learning using weakly supervised data

Efficient multiple instance metric learning using weakly supervised data
Marc Law, Yaoling Yu, Raquel Urtasun, Richard Zemel, Eric Xing
Paper BibTeX

Teaser for End-to-end instance segmentation with recurrent attention

End-to-end instance segmentation with recurrent attention
Mengye Ren, Richard Zemel
Paper BibTeX

Teaser for Few-shot learning through an information retrieval lens

Few-shot learning through an information retrieval lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Paper BibTeX

Teaser for Learning to generate images with perceptual similarity metrics

Learning to generate images with perceptual similarity metrics
Jake Snell, Karl Ridgeway, Renjie Liao, Brett Roads, Michael Mozer & Richard Zemel
Paper BibTeX

Teaser for Normalizing the normalizers: Comparing and extending network normalization schemes

Normalizing the normalizers: Comparing and extending network normalization schemes
Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian Sinz, Richard Zemel
Paper BibTeX

Teaser for Prototypical networks for few-shot learning

Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, Richard Zemel
Paper BibTeX

Teaser for Stochastic segmentation trees

Stochastic segmentation trees
Jake Snell, Richard Zemel
Paper BibTeX

2016

Teaser for Classifying NBA offensive plays using neural networks

Classifying NBA offensive plays using neural networks
Kuan-Chieh Wang, Richard Zemel
Paper BibTeX

Teaser for Gated graph sequence neural networks

Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
Paper BibTeX

Teaser for Learning deep parsimonious representations

Learning deep parsimonious representations
Renjie Liao, Alexander Schwing, Richard Zemel, Raquel Urtasun
Paper BibTeX

Teaser for The variational fair autoencoder

The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel
Paper BibTeX

Teaser for Towards generalizable sentence embeddings

Towards generalizable sentence embeddings
Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard Zemel
Paper BibTeX

Teaser for Training deep neural networks via direct loss minimization

Training deep neural networks via direct loss minimization
Yang Song, Alex Schwing, Richard Zemel, Raquel Urtasun
Paper BibTeX

Teaser for Understanding the effective receptive field in deep convolutional neural networks

Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel
Paper BibTeX

2015

Teaser for Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
Paper BibTeX

Teaser for Exploring Models and Data for Image Question Answering

Exploring Models and Data for Image Question Answering
Mengye Ren, Ryan Kiros, Richard Zemel
Paper BibTeX

Teaser for Generative Moment Matching Networks

Generative Moment Matching Networks
Yujia Li, Kevin Swersky, Richard Zemel
Paper BibTeX

Teaser for Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
Paper BibTeX

Skip-Thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
Paper BibTeX

2014

Teaser for A Multiplicative Model for Learning Distributed Text-Based Attribute Representations

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov
Paper BibTeX

Teaser for Input Warping for Bayesian Optimization of Non-stationary Functions

Input Warping for Bayesian Optimization of Non-stationary Functions
Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
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Learning unbiased features
Yujia Li, Kevin Swersky, Richard Zemel
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Mean-Field Networks
Yujia Li, Richard Zemel
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Teaser for Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel
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2013

Teaser for Learning Fair Representations

Learning Fair Representations
Richard Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork
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2012

Teaser for Active Learning for Matching Problems

Active Learning for Matching Problems
Laurent Charlin, Richard Zemel, Craig Boutilier
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Fairness Through Awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel
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Teaser for Fast Exact Inference for Recursive Cardinality Models

Fast Exact Inference for Recursive Cardinality Models
Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
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2011

Teaser for A Framework for Optimizing Paper Matching

A Framework for Optimizing Paper Matching
Laurent Charlin, Richard S. Zemel, Craig Boutilier
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Teaser for Interpreting Graph Cuts as a Max-Product Algorithm

Interpreting Graph Cuts as a Max-Product Algorithm
Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
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Teaser for Loss-sensitive Training of Probabilistic Conditional Random Fields

Loss-sensitive Training of Probabilistic Conditional Random Fields
Maksims N. Volkovs, Hugo Larochelle, Richard S. Zemel
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Teaser for Ranking via Sinkhorn Propagation

Ranking via Sinkhorn Propagation
Ryan Prescott Adams, Richard S. Zemel
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2008

Teaser for Flexible Priors for Exemplar-based Clustering

Flexible Priors for Exemplar-based Clustering
Daniel Tarlow, Richard S. Zemel, Brendan J. Frey
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2007

Teaser for Collaborative Filtering and the Missing at Random Assumption

Collaborative Filtering and the Missing at Random Assumption
Benjamin Marlin, Richard S. Zemel, Sam Roweis, Malcolm Slaney
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2003

Teaser for Active Collaborative Filtering

Active Collaborative Filtering
Craig Boutilier, Richard S. Zemel, Benjamin Marlin
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Teaser for Efficient Parametric Projection Pursuit Density Estimation

Efficient Parametric Projection Pursuit Density Estimation
Max Welling, Richard S. Zemel, Geoffrey E. Hinton
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