Zemel, Richard

68 publications

ICML 2025 Adaptive Elicitation of Latent Information Using Natural Language Jimmy Wang, Thomas P Zollo, Richard Zemel, Hongseok Namkoong

ICLRW 2025 Adaptive Elicitation of Latent Information Using Natural Language Jimmy Wang, Thomas P Zollo, Richard Zemel, Hongseok Namkoong

NeurIPS 2025 Guiding LLM Decision-Making with Fairness Reward Models Zara Hall, Melanie Subbiah, Thomas P Zollo, Kathleen McKeown, Richard Zemel

ICML 2025 QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions Zhun Deng, Thomas P Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard Zemel

ICLRW 2025 Societal Alignment Frameworks Can Improve LLM Alignment Karolina Stanczak, 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 Marasovic, Sylvie Delacroix, Gillian K Hadfield, Siva Reddy

ICLRW 2025 Towards Effective Discrimination Testing for Generative AI Thomas P Zollo, Nikita Rajaneesh, Richard Zemel, Talia B. Gillis, Emily Black

ECCV 2024 Controlling the World by Sleight of Hand Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick, Carl Vondrick, Richard Zemel

TMLR 2024 Improving Predictor Reliability with Selective Recalibration Thomas P Zollo, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel

CoLLAs 2024 Integrating Present and past in Unsupervised Continual Learning Yipeng Zhang, Laurent Charlin, Richard Zemel, Mengye Ren

ICML 2024 Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard Zemel

ICLR 2024 Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models Thomas P Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel

NeurIPSW 2023 ICL-Markup: Structuring In-Context Learning Using Soft-Token Tags Marc-Etienne Brunet, Ashton Anderson, Richard Zemel

NeurIPSW 2023 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

NeurIPSW 2023 ObEy: Quantifiable Object-Based Explainability Without Ground-Truth Annotations Lennart Schulze, William Ho, Richard Zemel

NeurIPSW 2023 Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models Thomas Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard Zemel

ICLR 2023 Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions Jake Snell, Thomas P Zollo, Zhun Deng, Toniann Pitassi, Richard Zemel

ICCV 2023 SurfsUP: Learning Fluid Simulation for Novel Surfaces Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard Zemel

CLeaR 2022 Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data Sindy Löwe, David Madras, Richard Zemel, Max Welling

CoLLAs 2022 Disentanglement and Generalization Under Correlation Shifts Christina M. Funke, Paul Vicol, Kuan-chieh Wang, Matthias Kuemmerer, Richard Zemel, Matthias Bethge

ICLRW 2022 Disentanglement and Generalization Under Correlation Shifts Christina M Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kuemmerer, Richard Zemel, Matthias Bethge

ICMLW 2022 Towards Environment-Invariant Representation Learning for Robust Task Transfer Benjamin Eyre, Richard Zemel, Elliot Creager

ICLR 2021 A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Renjie Liao, Raquel Urtasun, Richard Zemel

ICLR 2021 Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake Snell, Richard Zemel

ICML 2021 Environment Inference for Invariant Learning Elliot Creager, Joern-Henrik Jacobsen, Richard Zemel

ICML 2021 Learning a Universal Template for Few-Shot Dataset Generalization Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin

UAI 2021 NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao

ICML 2021 On Monotonic Linear Interpolation of Neural Network Parameters James R Lucas, Juhan Bae, Michael R Zhang, Stanislav Fort, Richard Zemel, Roger B Grosse

ICML 2021 SketchEmbedNet: Learning Novel Concepts by Imitating Drawings Alexander Wang, Mengye Ren, Richard Zemel

ICLR 2021 Theoretical Bounds on Estimation Error for Meta-Learning James Lucas, Mengye Ren, Irene Raissa KAMENI Kameni, Toniann Pitassi, Richard Zemel

NeurIPSW 2021 Understanding Post-Hoc Adaptation for Improving Subgroup Robustness David Madras, Richard Zemel

ICLR 2021 Wandering Within a World: Online Contextualized Few-Shot Learning Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard Zemel

ICML 2020 Causal Modeling for Fairness in Dynamical Systems Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel

ICML 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models Without Sampling Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel

ICML 2020 Optimizing Long-Term Social Welfare in Recommender Systems: A Constrained Matching Approach Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier

ICLR 2020 Understanding the Limitations of Conditional Generative Models Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard Zemel

CoRL 2019 A Divergence Minimization Perspective on Imitation Learning Methods Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu

ICLR 2019 Aggregated Momentum: Stability Through Passive Damping James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse

NeurIPS 2019 Efficient Graph Generation with Graph Recurrent Attention Networks Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K. Duvenaud, Raquel Urtasun, Richard Zemel

ICLR 2019 Excessive Invariance Causes Adversarial Vulnerability Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge

ICML 2019 Flexibly Fair Representation Learning by Disentanglement Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

NeurIPS 2019 Incremental Few-Shot Learning with Attention Attractor Networks Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel

ICLR 2019 LanczosNet: Multi-Scale Deep Graph Convolutional Networks Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel

ICML 2019 Lorentzian Distance Learning for Hyperbolic Representations Marc Law, Renjie Liao, Jake Snell, Richard Zemel

NeurIPS 2019 SMILe: Scalable Meta Inverse Reinforcement Learning Through Context-Conditional Policies Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard Zemel

ICML 2019 Understanding the Origins of Bias in Word Embeddings Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel

ICLRW 2019 Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning Seyed Kamyar Seyed Ghasemipour, Shane Gu, 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

NeurIPS 2018 Learning Latent Subspaces in Variational Autoencoders Jack Klys, Jake Snell, Richard Zemel

NeurIPS 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

ICML 2018 Neural Relational Inference for Interacting Systems Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

NeurIPS 2018 Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer David Madras, Toni Pitassi, Richard Zemel

ICML 2018 Reviving and Improving Recurrent Back-Propagation Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel

NeurIPS 2017 Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling

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

NeurIPS 2017 Few-Shot Learning Through an Information Retrieval Lens Eleni Triantafillou, Richard Zemel, Raquel Urtasun

NeurIPS 2017 Prototypical Networks for Few-Shot Learning Jake Snell, Kevin Swersky, Richard Zemel

NeurIPS 2016 Learning Deep Parsimonious Representations Renjie Liao, Alex Schwing, Richard Zemel, Raquel Urtasun

NeurIPS 2016 Understanding the Effective Receptive Field in Deep Convolutional Neural Networks Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel

NeurIPS 2015 Exploring Models and Data for Image Question Answering Mengye Ren, Ryan Kiros, Richard Zemel

NeurIPS 2015 Skip-Thought Vectors Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler

NeurIPS 2014 A Multiplicative Model for Learning Distributed Text-Based Attribute Representations Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov

NeurIPS 2013 A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams

CVPR 2013 Exploring Compositional High Order Pattern Potentials for Structured Output Learning Yujia Li, Daniel Tarlow, Richard Zemel

NeurIPS 2013 On the Representational Efficiency of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel

AISTATS 2012 Randomized Optimum Models for Structured Prediction Daniel Tarlow, Ryan Adams, Richard Zemel

AISTATS 2012 Structured Output Learning with High Order Loss Functions Daniel Tarlow, Richard Zemel

AISTATS 2010 HOP-MAP: Efficient Message Passing with High Order Potentials Daniel Tarlow, Inmar Givoni, Richard Zemel