Richard S. Zemel

ORCID

Publication Activity (10 Years)

Years Active: 1989-2024
Publications (10 Years): 160

Top Venues

Publications

Elliot Creager, Richard S. Zemel
Online Algorithmic Recourse by Collective Action. CoRR (2024)
Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Jwala Dhamala, Shalini Ghosh, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
JAB: Joint Adversarial Prompting and Belief Augmentation. CoRR (2023)
Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta
Are you talking to ['xem'] or ['x', 'em']? On Tokenization and Addressing Misgendering in LLMs with Pronoun Tokenization Parity. CoRR (2023)
Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel
ICL Markup: Structuring In-Context Learning using Soft-Token Tags. CoRR (2023)
Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation. CoRR (2023)
Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. ICLR (2023)
Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel
SURFSUP: Learning Fluid Simulation for Novel Surfaces. CoRR (2023)
Junyi Li, Ninareh Mehrabi, Charith Peris, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
On the steerability of large language models toward data-driven personas. CoRR (2023)
Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel
Distribution-Free Statistical Dispersion Control for Societal Applications. NeurIPS (2023)
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
FLIRT: Feedback Loop In-context Red Teaming. CoRR (2023)
Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard S. Zemel
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. CoRR (2023)
Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation. FAccT (2023)
Rahul Gupta, Lisa Bauer, Kai-Wei Chang, Jwala Dhamala, Aram Galstyan, Palash Goyal, Qian Hu, Avni Khatri, Rohit Parimi, Charith Peris, Apurv Verma, Richard S. Zemel, Prem Natarajan
Incorporating Fairness in Large Scale NLU Systems. WSDM (2023)
Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. CoRR (2023)
Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard S. Zemel, Rahul Gupta
Coordinated Replay Sample Selection for Continual Federated Learning. CoRR (2023)
Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel
SurfsUp: Learning Fluid Simulation for Novel Surfaces. ICCV (2023)
Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard S. Zemel, Rahul Gupta
Coordinated Replay Sample Selection for Continual Federated Learning. EMNLP (Industry Track) (2023)
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta
Resolving Ambiguities in Text-to-Image Generative Models. ACL (1) (2023)
Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard S. Zemel
Distribution-Free Statistical Dispersion Control for Societal Applications. CoRR (2023)
Lachlan McCalman, Daniel Steinberg, Grace Abuhamad, Marc-Etienne Brunet, Robert C. Williamson, Richard S. Zemel, Hsiao-Ying Lin
Assessing AI Fairness in Finance. Computer 55 (1) (2022)
Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel
Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. CoRR (2022)
Zhewei Sun, Richard S. Zemel, Yang Xu
Semantically Informed Slang Interpretation. NAACL-HLT (2022)
Zhewei Sun, Richard S. Zemel, Yang Xu
Semantically Informed Slang Interpretation. CoRR (2022)
António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard S. Zemel
Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic. LT-EDI (2022)
Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani
Variational Model Inversion Attacks. CoRR (2022)
Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel
Implications of Model Indeterminacy for Explanations of Automated Decisions. NeurIPS (2022)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
Deep Ensembles Work, But Are They Necessary? NeurIPS (2022)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
Deep Ensembles Work, But Are They Necessary? CoRR (2022)
Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard S. Zemel
Differentially Private Decoding in Large Language Models. CoRR (2022)
António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard S. Zemel
Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic. CoRR (2022)
Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge
Disentanglement and Generalization Under Correlation Shifts. CoLLAs (2022)
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models. CoRR (2022)
Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin
Learning a Universal Template for Few-shot Dataset Generalization. CoRR (2021)
Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard S. Zemel
Wandering within a world: Online contextualized few-shot learning. ICLR (2021)
Jake Snell, Richard S. Zemel
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. ICLR (2021)
David Madras, Richard S. Zemel
Identifying and Benchmarking Natural Out-of-Context Prediction Problems. NeurIPS (2021)
Zhewei Sun, Richard S. Zemel, Yang Xu
A Computational Framework for Slang Generation. Trans. Assoc. Comput. Linguistics 9 (2021)
James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR (2021)
Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. UAI (2021)
James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
On Monotonic Linear Interpolation of Neural Network Parameters. ICML (2021)
Jacob Kelly, Richard S. Zemel, Will Grathwohl
Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data. CoRR (2021)
David Madras, Richard S. Zemel
Identifying and Benchmarking Natural Out-of-Context Prediction Problems. CoRR (2021)
Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel
Environment Inference for Invariant Learning. ICML (2021)
Renjie Liao, Raquel Urtasun, Richard S. Zemel
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. ICLR (2021)
Zhewei Sun, Richard S. Zemel, Yang Xu
A Computational Framework for Slang Generation. CoRR (2021)
Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel
Online Unsupervised Learning of Visual Representations and Categories. CoRR (2021)
Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge
Disentanglement and Generalization Under Correlation Shifts. CoRR (2021)
Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin
Learning a Universal Template for Few-shot Dataset Generalization. ICML (2021)
Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani
Variational Model Inversion Attacks. NeurIPS (2021)
James Lucas, Mengye Ren, Irene Raissa Kameni, Toniann Pitassi, Richard S. Zemel
Theoretical bounds on estimation error for meta-learning. ICLR (2021)
Alexander Wang, Mengye Ren, Richard S. Zemel
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. ICML (2021)
Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao
NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation. CoRR (2021)
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR (2020)
Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel
Wandering Within a World: Online Contextualized Few-Shot Learning. CoRR (2020)
Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
Causal Modeling for Fairness In Dynamical Systems. ICML (2020)
Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. CoRR (2020)
James Lucas, Mengye Ren, Irene Kameni, Toniann Pitassi, Richard S. Zemel
Theoretical bounds on estimation error for meta-learning. CoRR (2020)
Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CoRR (2020)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. ICML (2020)
Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. CoRR (2020)
Jake Snell, Richard S. Zemel
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. CoRR (2020)
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard S. Zemel
Flexible Few-Shot Learning with Contextual Similarity. CoRR (2020)
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Shortcut learning in deep neural networks. Nat. Mach. Intell. 2 (11) (2020)
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel
Understanding the Limitations of Conditional Generative Models. ICLR (2020)
Renjie Liao, Raquel Urtasun, Richard S. Zemel
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. CoRR (2020)
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML (2020)
Alexander Wang, Mengye Ren, Richard S. Zemel
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. CoRR (2020)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. CoRR (2020)
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Shortcut Learning in Deep Neural Networks. CoRR (2020)
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel
Understanding the Origins of Bias in Word Embeddings. ICML (2019)
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR (Poster) (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR (2019)
Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning. DGS@ICLR (2019)
Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. ICLR (Poster) (2019)
James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse
Aggregated Momentum: Stability Through Passive Damping. ICLR (Poster) (2019)
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel
Incremental Few-Shot Learning with Attention Attractor Networks. NeurIPS (2019)
Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu
A Divergence Minimization Perspective on Imitation Learning Methods. CoRR (2019)
David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. FAT (2019)
Zhewei Sun, Richard S. Zemel, Yang Xu
Slang Generation as Categorization. CogSci (2019)
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard S. Zemel, Shengyu Zhang
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. CoRR (2019)
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
Inference in Probabilistic Graphical Models by Graph Neural Networks. ACSSC (2019)
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
Flexibly Fair Representation Learning by Disentanglement. ICML (2019)
Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel
Lorentzian Distance Learning for Hyperbolic Representations. ICML (2019)
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. CoRR (2019)
Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu
A Divergence Minimization Perspective on Imitation Learning Methods. CoRL (2019)
Amir Rosenfeld, Richard S. Zemel, John K. Tsotsos
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks. CoRR (2019)
Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge
Excessive Invariance Causes Adversarial Vulnerability. ICLR (Poster) (2019)
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
Flexibly Fair Representation Learning by Disentanglement. CoRR (2019)
Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. NeurIPS (2019)
Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
Causal Modeling for Fairness in Dynamical Systems. CoRR (2019)
Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel
Conditional Generative Models are not Robust. CoRR (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS (2019)
Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
Adversarial Distillation of Bayesian Neural Network Posteriors. ICML (2018)
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
Inference in probabilistic graphical models by Graph Neural Networks. ICLR (Workshop) (2018)
David Madras, Toniann Pitassi, Richard S. Zemel
Predict Responsibly: Increasing Fairness by Learning to Defer. ICLR (Workshop) (2018)
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR (2018)
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
Graph Partition Neural Networks for Semi-Supervised Classification. ICLR (Workshop) (2018)
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel
Neural Guided Constraint Logic Programming for Program Synthesis. NeurIPS (2018)
Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel
Neural Relational Inference for Interacting Systems. CoRR (2018)
Jack Klys, Jake Snell, Richard S. Zemel
Learning Latent Subspaces in Variational Autoencoders. NeurIPS (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Richard S. Zemel