Summary

Summary

I am a professor in the Department of Computer Science at Columbia University. I am broadly interested in machine learning, artificial intelligence, statistics, neuroscience, and cognitive science. I am also the Director of the new NSF AI Institute for ARtificial and Natural Intelligence (ARNI).

My recent research interests include:

• Robust AI
• Few-shot Learning
• Algorithmic Fairness
• Continual Learning

Office hours

During the academic year I hold weekly office hours. For the Spring 2024 term these are Thursdays 3-4PM (starting in March). For students and postdocs, coming to my office hours is easier than using email to make an appointment.

Teaching

Current:

• Fall/2023: COMS 4995, Neural Networks and Deep Learning

  Past:

• COMS 6998 (grad), Fair & Robust Algorithms
• COMS 6998 (grad), Self Supervised Learning
• CSC 311, Introduction to Machine Learning
• CSC 411, Machine Learning and Data Mining
• CSC 412/2506, Probabilistic Graphical Models
• CSC 2515, Introduction to Machine Learning
• CSC 2541, AI and Ethics: Mathematical Foundations and Algorithms
• CSC 2546, Computational Neuroscience

Talks

Here are some recent talks:

• Building Agile Machine Learning Models
• Few-Shot Learning in Context
• Learning Controllable Representations
• Ensuring Fair and Responsible Decision-Making

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Research Group

Students and Postdocs:

Marc-Etienne Brunet

Elliot Creager

Zhun Deng

Ben Eyre

Arjun Mani

Sruthi Sudhakar

Tom Zollo

Former students and postdocs:

Arnold Binas   (Senior Engineering Manager, Google)

Sagan Bolliger   (Master of Counselling Pschology, Adler University)

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

Laurent Charlin   (Associate Professor, University of Montreal)

Stephen Cowen   (Associate Professor, University of Arizona}

Emily Denton   (Senior Research Scientist, Google)

Stephen Fung   (Engineer, Google)

Ethan Fetaya   (Assistant Professor, Bar-Ilan University)

Kamyar Seyed Ghasemipour   (Research Engineer, Google)

Will Grathwohl   (Research Scientist, DeepMind)

Amit Gruber   (Research Scientist, IBM)

Xuming He   (Associate Professor, ShanghaiTech University)

Jorn Jacobsen   (Senior Research Scientist, Apple)

Nikola Karamanov   (Engineer, Visteon)

Jamie Ryan Kiros   (Entrepeneur, Latvia)

James Lucas   (Research Scientist, NVIDIA)

Gregory Koch  

Marc Law   (Senior Research Scientist, NVIDIA)

Yujia Li   (Research Scientist, DeepMind)

Renjie Liao   (Assistant Professor, University of British Columbia)

Jake Snell   (Postdoc, Princeton University)

David Madras   (Research Scientist, Google)

Benjamin Marlin   (Associate Professor, University of Massachusetts, Amherst)

James Martens   (Staff Research Scientist, DeepMind)

Rama Natarajan  

Jonathan Pillow   (Professor, Princeton University)

Mengye Ren   (Visiting Faculty Researcher, Google)

Danny Roobaert   (Founder, Lemaitre Capital)

David Ross   (Engineering Manager, Google Research)

Tanya Schmah   (Associate Professor, University of Ottawa)

Liam Stewart   (Software Engineer, Google)

Kevin Swersky   (Research Scientist, Google Brain)

Danny Tarlow   (Research Scientist, Google Brain)

David Towers  

Eleni Triantafillou   (Research Scientist, Google Brain)

Maks Volkovs   (Co-Founder, Layer 6 AI)

Alexander Wang   (PhD student, NYU)

Jackson Wang   (Postdoctoral Fellow, Stanford University)

Zhiyong Yang  

Lisa Zhang   (Assistant Professor, University of Toronto)

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Prospective PhD Students

If you are interested in applying for a PhD in Machine Learning at Columbia, you should apply through the Columbia University Computer Science department.

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Publications (from 2016-2023)

2023:

Distribution-free statistical dispersion control for societal applications
Zhun Deng, Thomas Zollo, Jake Snell, Toniann Pitassi, Richard Zemel
NeurIPS, 2023.

ICL Markup: Structuring in-context learning using soft-token tags
Marc-Etienne Brunet, Ashton Anderson, Richard Zemel
NeurIPS: R0-FoMo Workshop, 2023.

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
NeurIPS SoLaR Workshop, 2023.

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
CIKM, 2023.

Coordinated replay sample selection for continual federated learning
Jack Good and Jimit Majmudar and Christophe Dupuy and Jixuan Wang and Charith Peris and Clement Chung and Richard Zemel and Rahul Gupta
EMNLP, 2023.

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
ACL, 2023.

SurfsUp: Learning fluid simulation for novel surfaces
Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard Zemel
ICCV, 2023.

"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
FAccT, 2023.

Differentially private decoding in large language models
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard Zemel
NAACL TrustNLP Workshop, 2023.

Semantically informed slang interpretation
Zhewei Sun, Richard Zemel, Yang Xu
NAACL, 2023.

2022:

Implications of model indeterminacy for explanations of automated decisions
Marc-Etienne Brunet, Ashton Anderson, Richard Zemel
NeurIPS, 2022.

Deep ensembles work, but are they necessary?
Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, Richard Zemel, John Cunningham
NeurIPS, 2022.

Amortized Causal Discovery: Learning to infer causal graphs from time-series data
Sindy Lowe, David Madras, Richard Zemel, Max Welling
CLeaR, 2022.

Correlation and generalization under correlation shifts
Christina Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kummerer, Richard Zemel, Matthias Bethge
CoLLaS, 2022.

2021:

Identifying and benchmarking natural out-of-context prediction problems
David Madras, Richard Zemel
NeurIPS, 2021.

Directly training joint energy-based models for conditional synthesis and calibrated prediction of multi-attribute data
Jacob Kelly, Richard Zemel, Will Grathwohl
ICML UDL, 2021.

NP-DRAW: A non-parametric structured latent variable model for image generation
Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao
UAI, 2021.

Fairness and robustness in invariant learning: A case study in toxicity classification
Robert Adragna, Elliot Creager, David Madras, Richard Zemel
NeurIPS Workshop: Algorithmic Fairness through the Lens of Causality and Interpretability, 2021.

Environment inference for invariant learning
Elliot Creager, Jorn Jacobsen, Richard Zemel.
ICML, 2021.

SketchEmbedNet: Learning novel concepts by imitating drawings
Alex Wang, Mengye Ren, Richard Zemel
ICML, 2021.

Universal template for few-shot dataset generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin
ICML, 2021.

On monotonic linear interpolation of neural network parameters
James Lucas, Juhan Bae, Michael Zhang, Stanislav Fort, Richard Zemel, Roger Grosse
ICML, 2021

A computational framework for slang generation
Zhewei Sun, Richard Zemel, Yang Xu
Transactions of the Association for Computational Linguistics, 9: 478-462 (2021).

Wandering within a world: Online contextualized few-shot learning
Mengye Ren, Michael Iuzzolino, Michael Mozer, Richard Zemel
ICLR, 2021.

Bayesian few-shot classification with one-vs-each Polya-Gamma augmented Gaussian Processes
Jake Snell, Richard Zemel.
ICLR, 2021.

Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Kameni, Toni Pitassi, Richard Zemel
ICLR, 2021.

A PAC-Bayesian approach to generalization bounds for graph neural networks
Renjie Liao, Raquel Urtasun, Richard Zemel
ICLR, 2021.

2020:

Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix Wichmann
Nature Machine Intelligence: 2, 2020.

Causal modeling for fairness in dynamical systems
Elliot Creager, David Madras, Toni Pitassi, Richard Zemel
ICML, 2020.

Cutting out the middle-man: Training and evaluating energy-based models
Will Grathwohl, Jackson Wang, Jorn Jacobsen, David Duvenaud, Richard Zemel
ICML, 2020.

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
ICML, 2020.

Understanding the limitations of conditional generative models
Ethan Fetaya, Joern-Henrik Jacobsen, Will Grathwohl, Richard Zemel
ICLR, 2020.

2019:

A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shane Gu
CORL, 2019.

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
NeurIPS, 2019.

SMILe: Scalable meta inverse reinforcement learning through context-conditional policies
Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard Zemel
NeurIPS, 2019.

Incremental few-shot learning with attention attractor networks
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel
NeurIPS, 2019.

Understanding the origins of bias in word embedding
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel
ICML, 2019.

Lorentzian distance learning for hyperbolic representations
Marc Law, Renjie Liao, Jake Snell, Richard Zemel
ICML, 2019.

Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel
ICML, 2019.

Dimensionality reduction for representing the knowledge of probabilistic models
Marc Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard Zemel
ICLR, 2019.

Aggregated momentum: Stability through passive damping
James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse
ICLR, 2019.

Excessive invariance causes adversarial vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge
ICLR, 2019.

LanczosNet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel
ICLR, 2019.

Fairness through causal awareness: Learning causal latent-variable models for biased data.
David Madras, Elliot Creager, Toni Pitassi, Richard Zemel
FAccT, 2019.

2018:

Neural guided constraint logic programming for program synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel.
NeurIPS, 2018.

Predict responsibly: improving fairness and accuracy by learning to defer
David Madras, Toni Pitassi, Richard Zemel
NeurIPS, 2018.

Learning latent subspaces in variational autoencoders
Jack Klys, Jake Snell, Richard Zemel
NeurIPS, 2018.

Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel
ICML, 2018.

Adversarial distillation of Bayesian neural network posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel
ICML, 2018.

Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel
ICML, 2018.

Reviving and improving recurrent back-propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Zachary Pitkow, Raquel Urtasun, Richard Zemel
ICML, 2018.

The elephant in the room
Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Arxiv, 2018.

2017:

Few-shot learning through an information retrieval lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
NeurIPS, 2017.

Dualing GANs
Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel
NeurIPS, 2017.

Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling
NeurIPS, 2017.

Deep spectral clustering learning
Marc Law, Raquel Urtasun, Richard Zemel
ICML, 2017.

Efficient multiple instance metric learning using weakly supervised data
Marc Law, Yaoling Yu, Raquel Urtasun, Richard Zemel, Eric Xing
CVPR, 2017.

Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, Richard Zemel
NeurIPS, 2017.

Stochastic segmentation trees
Jake Snell, Richard Zemel
UAI, 2017.

Learning to generate images with perceptual similarity metrics
Jake Snell, Karl Ridgeway, Renjie Liao, Brett Roads, Michael Mozer & Richard Zemel
ICIP, 2017.

Normalizing the normalizers: Comparing and extending network normalization schemes
Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian Sinz, Richard Zemel
ICLR, 2017.

End-to-end instance segmentation with recurrent attention
Mengye Ren and Richard Zemel
CVPR, 2017.

Towards generalizable sentence embeddings
Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard Zemel
ACL Workshop on Representation Learning for NLP, 2017.

2016:

The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard Zemel
ICLR, 2016.

Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
ICLR, 2016.

Training deep neural networks via direct loss minimization
Yang Song, Alex Schwing, Richard Zemel, Raquel Urtasun
ICLR, 2016.

Classifying NBA offensive plays using neural networks
Kuan-Chieh Wang, Richard Zemel
Sloan Sports Analytics Conference, 2016.

Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel
NeurIPS, 2016.

Learning deep parsimonious representations
Renjie Liao, Alexander Schwing, Richard Zemel, Raquel Urtasun
NeurIPS, 2016.