Richard S. Zemel

303 papers A* 61A 20B 3Misc 2Journal 140Unranked 72

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Few-Shot Design Optimization by Exploiting Auxiliary Information. CoRR Arjun Mani, Carl Vondrick, Richard S. Zemel
2026	J	`jnl`	Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning. CoRR Zhenwei Tang, Amogh Inamdar, Ashton Anderson, Richard S. Zemel
2026	J	`jnl`	Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language. CoRR Max S. Bennett, Thomas P. Zollo, Richard S. Zemel
2026	J	`jnl`	Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions. CoRR Ruomeng Ding, Tianwei Gao, Thomas P. Zollo, Eitan Bachmat, Richard S. Zemel, Zhun Deng
2025	A*	`conf`	Adaptive Elicitation of Latent Information Using Natural Language. ICML Jimmy Wang, Thomas P. Zollo, Richard S. Zemel, Hongseok Namkoong
2025	J	`jnl`	Adaptive Elicitation of Latent Information Using Natural Language. CoRR Jimmy Wang, Thomas P. Zollo, Richard S. Zemel, Hongseok Namkoong
2025	J	`jnl`	Confidence Calibration in Vision-Language-Action Models. CoRR Thomas P. Zollo, Richard S. Zemel
2025	J	`jnl`	Guiding LLM Decision-Making with Fairness Reward Models. CoRR Zara Hall, Melanie Subbiah, Thomas P. Zollo, Kathleen McKeown, Richard S. Zemel
2025	J	`jnl`	Let the Experts Speak: Improving Survival Prediction & Calibration via Mixture-of-Experts Heads. CoRR Todd Morrill, Aahlad Manas Puli, Murad Megjhani, Soojin Park, Richard S. Zemel
2025	A*	`conf`	QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. ICML Zhun Deng, Thomas P. Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard S. Zemel
2025	J	`jnl`	QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. CoRR Zhun Deng, Thomas P. Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard S. Zemel
2025	J	`jnl`	Replay Can Provably Increase Forgetting. CoRR Yasaman Mahdaviyeh, James Lucas, Mengye Ren, Andreas S. Tolias, Richard S. Zemel, Toniann Pitassi
2025	J	`jnl`	Schemex: Interactive Structural Abstraction from Examples with Contrastive Refinement. CoRR Sitong Wang, Samia Menon, Dingzeyu Li, Xiaojuan Ma, Richard S. Zemel, Lydia B. Chilton
2025	J	`jnl`	Societal Alignment Frameworks Can Improve LLM Alignment. CoRR Karolina Stanczak, Nicholas Meade, Mehar Bhatia, Hattie Zhou, Konstantin Böttinger, Jeremy Barnes, Jason Stanley, Jessica Montgomery, Richard S. Zemel, Nicolas Papernot, Nicolas Chapados, Denis Therien, Timothy P. Lillicrap, Ana Marasovic, Sylvie Delacroix, Gillian K. Hadfield, Siva Reddy
2025	J	`jnl`	Test-Time Warmup for Multimodal Large Language Models. CoRR Nikita Rajaneesh, Thomas P. Zollo, Richard S. Zemel
2025	—	`conf`	Towards Effective Discrimination Testing for Generative AI. FAccT Thomas P. Zollo, Nikita Rajaneesh, Richard S. Zemel, Talia Gillis, Emily Black
2025	—	`conf`	Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation. ACL (Findings) Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris
2025	J	`jnl`	Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation. CoRR Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris
2024	—	`conf`	Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification. EMNLP (Findings) Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard S. Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris
2024	J	`jnl`	Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification. CoRR Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard S. Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris
2024	—	`conf`	Controlling the World by Sleight of Hand. ECCV (30) Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick, Carl Vondrick, Richard S. Zemel
2024	J	`jnl`	Controlling the World by Sleight of Hand. CoRR Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick, Carl Vondrick, Richard S. Zemel
2024	A*	`conf`	FLIRT: Feedback Loop In-context Red Teaming. EMNLP Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
2024	J	`jnl`	Improving Predictor Reliability with Selective Recalibration. CoRR Thomas P. Zollo, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard S. Zemel
2024	J	`jnl`	Improving Predictor Reliability with Selective Recalibration. Trans. Mach. Learn. Res. Thomas P. Zollo, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel
2024	—	`conf`	Integrating Present and Past in Unsupervised Continual Learning. CoLLAs Yipeng Zhang, Laurent Charlin, Richard S. Zemel, Mengye Ren
2024	J	`jnl`	Integrating Present and Past in Unsupervised Continual Learning. CoRR Yipeng Zhang, Laurent Charlin, Richard S. Zemel, Mengye Ren
2024	J	`jnl`	Online Algorithmic Recourse by Collective Action. CoRR Elliot Creager, Richard S. Zemel
2024	A*	`conf`	Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. ICML Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
2024	J	`jnl`	Partial Federated Learning. CoRR Tiantian Feng, Anil Ramakrishna, Jimit Majmudar, Charith Peris, Jixuan Wang, Clement Chung, Richard S. Zemel, Morteza Ziyadi, Rahul Gupta
2024	A*	`conf`	Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. ICLR Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel
2024	—	`conf`	The steerability of large language models toward data-driven personas. NAACL-HLT Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
2024	—	`conf`	Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies. NAACL-HLT (Findings) Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
2024	—	`conf`	Toward Informal Language Processing: Knowledge of Slang in Large Language Models. NAACL-HLT Zhewei Sun, Qian Hu, Rahul Gupta, Richard S. Zemel, Yang Xu
2024	J	`jnl`	Toward Informal Language Processing: Knowledge of Slang in Large Language Models. CoRR Zhewei Sun, Qian Hu, Rahul Gupta, Richard S. Zemel, Yang Xu
2024	J	`jnl`	Towards Effective Discrimination Testing for Generative AI. CoRR Thomas P. Zollo, Nikita Rajaneesh, Richard S. Zemel, Talia B. Gillis, Emily Black
2024	A*	`conf`	Training-free Deep Concept Injection Enables Language Models for Video Question Answering. EMNLP Xudong Lin, Manling Li, Richard S. Zemel, Heng Ji, Shih-Fu Chang
2024	A*	`conf`	Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities. EMNLP Sachit Menon, Richard S. Zemel, Carl Vondrick
2024	J	`jnl`	Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities. CoRR Sachit Menon, Richard S. Zemel, Carl Vondrick
2023	—	`conf`	"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation. FAccT Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
2023	J	`jnl`	"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation. CoRR Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
2023	J	`jnl`	Are you talking to ['xem'] or ['x', 'em']? On Tokenization and Addressing Misgendering in LLMs with Pronoun Tokenization Parity. CoRR Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta
2023	—	`conf`	Coordinated Replay Sample Selection for Continual Federated Learning. EMNLP (Industry Track) Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard S. Zemel, Rahul Gupta
2023	J	`jnl`	Coordinated Replay Sample Selection for Continual Federated Learning. CoRR Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard S. Zemel, Rahul Gupta
2023	A*	`conf`	Distribution-Free Statistical Dispersion Control for Societal Applications. NeurIPS Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel
2023	J	`jnl`	Distribution-Free Statistical Dispersion Control for Societal Applications. CoRR Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard S. Zemel
2023	J	`jnl`	FLIRT: Feedback Loop In-context Red Teaming. CoRR Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
2023	J	`jnl`	ICL Markup: Structuring In-Context Learning using Soft-Token Tags. CoRR Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel
2023	A	`conf`	Incorporating Fairness in Large Scale NLU Systems. WSDM 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
2023	J	`jnl`	JAB: Joint Adversarial Prompting and Belief Augmentation. CoRR Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Jwala Dhamala, Shalini Ghosh, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
2023	J	`jnl`	On the steerability of large language models toward data-driven personas. CoRR Junyi Li, Ninareh Mehrabi, Charith Peris, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta
2023	J	`jnl`	Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. CoRR Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
2023	A	`conf`	Privacy in the Time of Language Models. WSDM Charith Peris, Christophe Dupuy, Jimit Majmudar, Rahil Parikh, Sami Smaili, Richard S. Zemel, Rahul Gupta
2023	J	`jnl`	Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. CoRR Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake C. Snell, Toniann Pitassi, Richard S. Zemel
2023	A*	`conf`	Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. ICLR Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel
2023	—	`conf`	Resolving Ambiguities in Text-to-Image Generative Models. ACL (1) Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta
2023	J	`jnl`	SURFSUP: Learning Fluid Simulation for Novel Surfaces. CoRR Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel
2023	A*	`conf`	SurfsUp: Learning Fluid Simulation for Novel Surfaces. ICCV Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel
2022	—	`conf`	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CLeaR Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
2022	J	`jnl`	Assessing AI Fairness in Finance. Computer Lachlan McCalman, Daniel Steinberg, Grace Abuhamad, Marc-Etienne Brunet, Robert C. Williamson, Richard S. Zemel
2022	A*	`conf`	Deep Ensembles Work, But Are They Necessary? NeurIPS Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
2022	J	`jnl`	Deep Ensembles Work, But Are They Necessary? CoRR Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
2022	J	`jnl`	Differentially Private Decoding in Large Language Models. CoRR Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard S. Zemel
2022	—	`conf`	Disentanglement and Generalization Under Correlation Shifts. CoLLAs Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge
2022	A*	`conf`	Implications of Model Indeterminacy for Explanations of Automated Decisions. NeurIPS Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel
2022	J	`jnl`	Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models. CoRR Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta
2022	—	`conf`	Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic. LT-EDI António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard S. Zemel
2022	J	`jnl`	Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic. CoRR António Câmara, Nina Taneja, Tamjeed Azad, Emily Allaway, Richard S. Zemel
2022	J	`jnl`	Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. CoRR Jake C. Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel
2022	—	`conf`	Semantically Informed Slang Interpretation. NAACL-HLT Zhewei Sun, Richard S. Zemel, Yang Xu
2022	J	`jnl`	Semantically Informed Slang Interpretation. CoRR Zhewei Sun, Richard S. Zemel, Yang Xu
2022	J	`jnl`	Variational Model Inversion Attacks. CoRR Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani
2021	J	`jnl`	A Computational Framework for Slang Generation. CoRR Zhewei Sun, Richard S. Zemel, Yang Xu
2021	J	`jnl`	A Computational Framework for Slang Generation. Trans. Assoc. Comput. Linguistics Zhewei Sun, Richard S. Zemel, Yang Xu
2021	A*	`conf`	A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. ICLR Renjie Liao, Raquel Urtasun, Richard S. Zemel
2021	J	`jnl`	Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
2021	A*	`conf`	Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. ICLR Jake Snell, Richard S. Zemel
2021	J	`jnl`	Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data. CoRR Jacob Kelly, Richard S. Zemel, Will Grathwohl
2021	J	`jnl`	Disentanglement and Generalization Under Correlation Shifts. CoRR Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge
2021	A*	`conf`	Environment Inference for Invariant Learning. ICML Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel
2021	—	`ed.`	FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021 FAccT Madeleine Clare Elish, William Isaac, Richard S. Zemel
2021	A*	`conf`	Identifying and Benchmarking Natural Out-of-Context Prediction Problems. NeurIPS David Madras, Richard S. Zemel
2021	J	`jnl`	Identifying and Benchmarking Natural Out-of-Context Prediction Problems. CoRR David Madras, Richard S. Zemel
2021	A*	`conf`	Learning a Universal Template for Few-shot Dataset Generalization. ICML Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin
2021	J	`jnl`	Learning a Universal Template for Few-shot Dataset Generalization. CoRR Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin
2021	A	`conf`	NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. UAI Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao
2021	J	`jnl`	NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation. CoRR Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao
2021	A*	`conf`	On Monotonic Linear Interpolation of Neural Network Parameters. ICML James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
2021	J	`jnl`	Online Unsupervised Learning of Visual Representations and Categories. CoRR Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel
2021	A*	`conf`	SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. ICML Alexander Wang, Mengye Ren, Richard S. Zemel
2021	A*	`conf`	Theoretical bounds on estimation error for meta-learning. ICLR James Lucas, Mengye Ren, Irene Raissa Kameni, Toniann Pitassi, Richard S. Zemel
2021	A*	`conf`	Variational Model Inversion Attacks. NeurIPS Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani
2021	A*	`conf`	Wandering within a world: Online contextualized few-shot learning. ICLR Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard S. Zemel
2020	J	`jnl`	A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. CoRR Renjie Liao, Raquel Urtasun, Richard S. Zemel
2020	J	`jnl`	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CoRR Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
2020	J	`jnl`	Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. CoRR Jake Snell, Richard S. Zemel
2020	A*	`conf`	Causal Modeling for Fairness In Dynamical Systems. ICML Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
2020	J	`jnl`	Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. CoRR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	J	`jnl`	Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. CoRR Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel
2020	J	`jnl`	Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. CoRR Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel
2020	J	`jnl`	Flexible Few-Shot Learning with Contextual Similarity. CoRR Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard S. Zemel
2020	A*	`conf`	Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. ICML Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	A*	`conf`	Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
2020	J	`jnl`	Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
2020	J	`jnl`	Shortcut Learning in Deep Neural Networks. CoRR Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
2020	J	`jnl`	Shortcut learning in deep neural networks. Nat. Mach. Intell. Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
2020	J	`jnl`	SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. CoRR Alexander Wang, Mengye Ren, Richard S. Zemel
2020	J	`jnl`	Theoretical bounds on estimation error for meta-learning. CoRR James Lucas, Mengye Ren, Irene Kameni, Toniann Pitassi, Richard S. Zemel
2020	A*	`conf`	Understanding the Limitations of Conditional Generative Models. ICLR Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel
2020	J	`jnl`	Wandering Within a World: Online Contextualized Few-Shot Learning. CoRR Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel
2019	Misc	`conf`	A Divergence Minimization Perspective on Imitation Learning Methods. CoRL Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu
2019	J	`jnl`	A Divergence Minimization Perspective on Imitation Learning Methods. CoRR Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu
2019	—	`conf`	Aggregated Momentum: Stability Through Passive Damping. ICLR (Poster) James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse
2019	J	`jnl`	Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. CoRR 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
2019	J	`jnl`	Causal Modeling for Fairness in Dynamical Systems. CoRR Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	Conditional Generative Models are not Robust. CoRR Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel
2019	—	`conf`	Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. ICLR (Poster) Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel
2019	A*	`conf`	Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
2019	J	`jnl`	Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
2019	—	`conf`	Excessive Invariance Causes Adversarial Vulnerability. ICLR (Poster) Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge
2019	—	`conf`	Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. FAT David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2019	A*	`conf`	Flexibly Fair Representation Learning by Disentanglement. ICML Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	Flexibly Fair Representation Learning by Disentanglement. CoRR Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks. CoRR Amir Rosenfeld, Richard S. Zemel, John K. Tsotsos
2019	A*	`conf`	Incremental Few-Shot Learning with Attention Attractor Networks. NeurIPS Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel
2019	Misc	`conf`	Inference in Probabilistic Graphical Models by Graph Neural Networks. ACSSC KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
2019	—	`conf`	LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR (Poster) Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
2019	J	`jnl`	LanczosNet: Multi-Scale Deep Graph Convolutional Networks. CoRR Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
2019	A*	`conf`	Lorentzian Distance Learning for Hyperbolic Representations. ICML Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel
2019	A*	`conf`	SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. NeurIPS Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel
2019	B	`conf`	Slang Generation as Categorization. CogSci Zhewei Sun, Richard S. Zemel, Yang Xu
2019	A*	`conf`	Understanding the Origins of Bias in Word Embeddings. ICML Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel
2019	—	`conf`	Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning. DGS@ICLR Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel
2018	A*	`conf`	Adversarial Distillation of Bayesian Neural Network Posteriors. ICML Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
2018	J	`jnl`	Adversarial Distillation of Bayesian Neural Network Posteriors. CoRR Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel
2018	J	`jnl`	Aggregated Momentum: Stability Through Passive Damping. CoRR James Lucas, Richard S. Zemel, Roger B. Grosse
2018	J	`jnl`	Excessive Invariance Causes Adversarial Vulnerability. CoRR Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge
2018	J	`jnl`	Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. CoRR David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2018	—	`conf`	Gradient-based Optimization of Neural Network Architecture. ICLR (Workshop) Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel
2018	—	`conf`	Graph Partition Neural Networks for Semi-Supervised Classification. ICLR (Workshop) Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
2018	J	`jnl`	Graph Partition Neural Networks for Semi-Supervised Classification. CoRR Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
2018	J	`jnl`	Incremental Few-Shot Learning with Attention Attractor Networks. CoRR Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel
2018	J	`jnl`	Inference in Probabilistic Graphical Models by Graph Neural Networks. CoRR KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
2018	—	`conf`	Inference in probabilistic graphical models by Graph Neural Networks. ICLR (Workshop) KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
2018	A*	`conf`	Learning Adversarially Fair and Transferable Representations. ICML David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2018	J	`jnl`	Learning Adversarially Fair and Transferable Representations. CoRR David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2018	A*	`conf`	Learning Latent Subspaces in Variational Autoencoders. NeurIPS Jack Klys, Jake Snell, Richard S. Zemel
2018	J	`jnl`	Learning Latent Subspaces in Variational Autoencoders. CoRR Jack Klys, Jake Snell, Richard S. Zemel
2018	—	`conf`	Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. ICLR (Workshop) Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel
2018	—	`conf`	Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
2018	J	`jnl`	Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
2018	A*	`conf`	Neural Guided Constraint Logic Programming for Program Synthesis. NeurIPS Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel
2018	J	`jnl`	Neural Guided Constraint Logic Programming for Program Synthesis. CoRR Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel
2018	A*	`conf`	Neural Relational Inference for Interacting Systems. ICML Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel
2018	J	`jnl`	Neural Relational Inference for Interacting Systems. CoRR Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel
2018	A*	`conf`	Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. NeurIPS David Madras, Toniann Pitassi, Richard S. Zemel
2018	—	`conf`	Predict Responsibly: Increasing Fairness by Learning to Defer. ICLR (Workshop) David Madras, Toniann Pitassi, Richard S. Zemel
2018	A*	`conf`	Reviving and Improving Recurrent Back-Propagation. ICML Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel
2018	J	`jnl`	Reviving and Improving Recurrent Back-Propagation. CoRR Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel
2018	J	`jnl`	The Elephant in the Room. CoRR Amir Rosenfeld, Richard S. Zemel, John K. Tsotsos
2018	J	`jnl`	Understanding the Origins of Bias in Word Embeddings. CoRR Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel
2017	—	`conf`	Causal Effect Inference with Deep Latent-Variable Models. NIPS Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	J	`jnl`	Causal Effect Inference with Deep Latent-Variable Models. CoRR Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling
2017	A*	`conf`	Deep Spectral Clustering Learning. ICML Marc T. Law, Raquel Urtasun, Richard S. Zemel
2017	—	`conf`	Dualing GANs. NIPS Yujia Li, Alexander G. Schwing, Kuan-Chieh Wang, Richard S. Zemel
2017	J	`jnl`	Dualing GANs. CoRR Yujia Li, Alexander G. Schwing, Kuan-Chieh Wang, Richard S. Zemel
2017	A*	`conf`	Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. CVPR Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing
2017	A*	`conf`	End-to-End Instance Segmentation with Recurrent Attention. CVPR Mengye Ren, Richard S. Zemel
2017	—	`conf`	Few-Shot Learning Through an Information Retrieval Lens. NIPS Eleni Triantafillou, Richard S. Zemel, Raquel Urtasun
2017	J	`jnl`	Few-Shot Learning Through an Information Retrieval Lens. CoRR Eleni Triantafillou, Richard S. Zemel, Raquel Urtasun
2017	—	`conf`	Joint Embeddings of Scene Graphs and Images. ICLR (Workshop) Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel
2017	B	`conf`	Learning to generate images with perceptual similarity metrics. ICIP Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
2017	—	`conf`	Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. ICLR (Poster) Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel
2017	J	`jnl`	Predict Responsibly: Increasing Fairness by Learning To Defer. CoRR David Madras, Toniann Pitassi, Richard S. Zemel
2017	—	`conf`	Prototypical Networks for Few-shot Learning. NIPS Jake Snell, Kevin Swersky, Richard S. Zemel
2017	J	`jnl`	Prototypical Networks for Few-shot Learning. CoRR Jake Snell, Kevin Swersky, Richard S. Zemel
2017	A	`conf`	Stochastic Segmentation Trees for Multiple Ground Truths. UAI Jake Snell, Richard S. Zemel
2017	J	`jnl`	Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. CoRR Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel
2016	J	`jnl`	End-to-End Instance Segmentation and Counting with Recurrent Attention. CoRR Mengye Ren, Richard S. Zemel
2016	A*	`conf`	Gated Graph Sequence Neural Networks. ICLR (Poster) Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel
2016	—	`conf`	Learning Deep Parsimonious Representations. NIPS Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun
2016	B	`conf`	Learning to generate images and their descriptions (keynote). ICMI Richard S. Zemel
2016	J	`jnl`	Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. CoRR Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel
2016	A*	`conf`	The Variational Fair Autoencoder. ICLR Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel
2016	—	`conf`	Towards Generalizable Sentence Embeddings. Rep4NLP@ACL Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel
2016	A*	`conf`	Training Deep Neural Networks via Direct Loss Minimization. ICML Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun
2016	—	`conf`	Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. NIPS Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel
2015	J	`jnl`	A Neural Autoregressive Approach to Attention-based Recognition. Int. J. Comput. Vis. Yin Zheng, Richard S. Zemel, Yu-Jin Zhang, Hugo Larochelle
2015	A*	`conf`	Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books. ICCV Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
2015	J	`jnl`	Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books. CoRR Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
2015	J	`jnl`	Direct Loss Minimization for Training Deep Neural Nets. CoRR Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun
2015	—	`conf`	Exploring Models and Data for Image Question Answering. NIPS Mengye Ren, Ryan Kiros, Richard S. Zemel
2015	A*	`conf`	Generative Moment Matching Networks. ICML Yujia Li, Kevin Swersky, Richard S. Zemel
2015	J	`jnl`	Generative Moment Matching Networks. CoRR Yujia Li, Kevin Swersky, Richard S. Zemel
2015	J	`jnl`	Guest Editors' Introduction: Special Section on Higher Order Graphical Models in Computer Vision. IEEE Trans. Pattern Anal. Mach. Intell. Karteek Alahari, Dhruv Batra, Srikumar Ramalingam, Nikos Paragios, Richard S. Zemel
2015	J	`jnl`	Image Question Answering: A Visual Semantic Embedding Model and a New Dataset. CoRR Mengye Ren, Ryan Kiros, Richard S. Zemel
2015	J	`jnl`	Learning to generate images with perceptual similarity metrics. CoRR Karl Ridgeway, Jake Snell, Brett Roads, Richard S. Zemel, Michael C. Mozer
2015	A*	`conf`	Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. ICML Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio
2015	J	`jnl`	Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. CoRR Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio
2015	—	`conf`	Skip-Thought Vectors. NIPS Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler
2015	J	`jnl`	Skip-Thought Vectors. CoRR Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
2014	—	`conf`	A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. NIPS Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov
2014	J	`jnl`	A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. CoRR Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov
2014	A*	`conf`	High Order Regularization for Semi-Supervised Learning of Structured Output Problems. ICML Yujia Li, Richard S. Zemel
2014	A*	`conf`	Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
2014	J	`jnl`	Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
2014	J	`jnl`	Learning unbiased features. CoRR Yujia Li, Kevin Swersky, Richard S. Zemel
2014	A*	`conf`	Leveraging user libraries to bootstrap collaborative filtering. KDD Laurent Charlin, Richard S. Zemel, Hugo Larochelle
2014	J	`jnl`	Mean-Field Networks. CoRR Yujia Li, Richard S. Zemel
2014	A*	`conf`	Multimodal Neural Language Models. ICML Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel
2014	J	`jnl`	New learning methods for supervised and unsupervised preference aggregation. J. Mach. Learn. Res. Maksims Volkovs, Richard S. Zemel
2014	J	`jnl`	Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. CoRR Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel
2013	—	`conf`	A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. NIPS Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams
2013	A	`conf`	CRF framework for supervised preference aggregation. CIKM Maksims Volkovs, Richard S. Zemel
2013	A*	`conf`	Exploring Compositional High Order Pattern Potentials for Structured Output Learning. CVPR Yujia Li, Daniel Tarlow, Richard S. Zemel
2013	—	`conf`	Learning Fair Representations. ICML (3) Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork
2013	—	`conf`	On the Expressive Power of Restricted Boltzmann Machines. NIPS James Martens, Arkadev Chattopadhyay, Toniann Pitassi, Richard S. Zemel
2013	—	`conf`	Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
2012	J	`jnl`	A Framework for Optimizing Paper Matching CoRR Laurent Charlin, Richard S. Zemel, Craig Boutilier
2012	A*	`conf`	A flexible generative model for preference aggregation. WWW Maksims Volkovs, Richard S. Zemel
2012	J	`jnl`	Active Collaborative Filtering CoRR Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin
2012	J	`jnl`	Active Learning for Matching Problems CoRR Laurent Charlin, Richard S. Zemel, Craig Boutilier
2012	A*	`conf`	Active Learning for Matching Problems. ICML Laurent Charlin, Richard S. Zemel, Craig Boutilier
2012	—	`conf`	Cardinality Restricted Boltzmann Machines. NIPS Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
2012	J	`jnl`	Collaborative Filtering and the Missing at Random Assumption CoRR Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney
2012	—	`conf`	Collaborative Ranking With 17 Parameters. NIPS Maksims Volkovs, Richard S. Zemel
2012	J	`jnl`	Efficient Parametric Projection Pursuit Density Estimation CoRR Max Welling, Richard S. Zemel, Geoffrey E. Hinton
2012	—	`conf`	Efficient Sampling for Bipartite Matching Problems. NIPS Maksims Volkovs, Richard S. Zemel
2012	A	`conf`	Fairness through awareness. ITCS Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel
2012	J	`jnl`	Fast Exact Inference for Recursive Cardinality Models CoRR Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
2012	A	`conf`	Fast Exact Inference for Recursive Cardinality Models. UAI Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey
2012	J	`jnl`	Flexible Priors for Exemplar-based Clustering CoRR Daniel Tarlow, Richard S. Zemel, Brendan J. Frey
2012	A	`conf`	Learning to rank by aggregating expert preferences. CIKM Maksims Volkovs, Hugo Larochelle, Richard S. Zemel
2012	—	`conf`	Probabilistic n-Choose-k Models for Classification and Ranking. NIPS Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey
2012	A	`conf`	Randomized Optimum Models for Structured Prediction. AISTATS Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel
2012	A	`conf`	Structured Output Learning with High Order Loss Functions. AISTATS Daniel Tarlow, Richard S. Zemel
2011	A	`conf`	A Framework for Optimizing Paper Matching. UAI Laurent Charlin, Richard S. Zemel, Craig Boutilier
2011	—	`ed.`	Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. NIPS John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger
2011	J	`jnl`	Fairness Through Awareness CoRR Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel
2011	A	`conf`	Graph Cuts is a Max-Product Algorithm. UAI Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
2011	J	`jnl`	Interpreting Graph Cuts as a Max-Product Algorithm CoRR Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
2011	J	`jnl`	Loss-sensitive Training of Probabilistic Conditional Random Fields CoRR Maksims Volkovs, Hugo Larochelle, Richard S. Zemel
2011	J	`jnl`	Ranking via Sinkhorn Propagation CoRR Ryan Prescott Adams, Richard S. Zemel
2011	A*	`conf`	Recommender Systems, Missing Data and Statistical Model Estimation. IJCAI Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney
2010	—	`ed.`	Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. NIPS John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta
2010	J	`jnl`	Comparing Classification Methods for Longitudinal fMRI Studies. Neural Comput. Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Strother
2010	A	`conf`	HOP-MAP: Efficient Message Passing with High Order Potentials. AISTATS Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel
2010	J	`jnl`	Learning Articulated Structure and Motion. Int. J. Comput. Vis. David A. Ross, Daniel Tarlow, Richard S. Zemel
2009	J	`jnl`	Automated detection of unusual events on stairs. Image Vis. Comput. Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel, Alex Mihailidis
2009	A*	`conf`	BoltzRank: learning to maximize expected ranking gain. ICML Maksims Volkovs, Richard S. Zemel
2009	A	`conf`	Collaborative prediction and ranking with non-random missing data. RecSys Benjamin M. Marlin, Richard S. Zemel
2008	—	`conf`	Characterizing response behavior in multisensory perception with conflicting cues. NIPS Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel
2008	J	`jnl`	Encoding and Decoding Spikes for Dynamic Stimuli. Neural Comput. Rama Natarajan, Quentin J. M. Huys, Peter Dayan, Richard S. Zemel
2008	A	`conf`	Flexible Priors for Exemplar-based Clustering. UAI Daniel Tarlow, Richard S. Zemel, Brendan J. Frey
2008	—	`conf`	Generative versus discriminative training of RBMs for classification of fMRI images. NIPS Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother
2008	A*	`conf`	Latent topic random fields: Learning using a taxonomy of labels. CVPR Xuming He, Richard S. Zemel
2008	J	`jnl`	Learning Flexible Features for Conditional Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. Liam Stewart, Xuming He, Richard S. Zemel
2008	—	`conf`	Learning Hybrid Models for Image Annotation with Partially Labeled Data. NIPS Xuming He, Richard S. Zemel
2008	A*	`conf`	Learning stick-figure models using nonparametric Bayesian priors over trees. CVPR Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis
2008	J	`jnl`	Population Coding with Motion Energy Filters: The Impact of Correlations. Neural Comput. F. Klam, Richard S. Zemel, Alexandre Pouget
2008	—	`conf`	Unsupervised Learning of Skeletons from Motion. ECCV (3) David A. Ross, Daniel Tarlow, Richard S. Zemel
2007	A	`conf`	Collaborative Filtering and the Missing at Random Assumption. UAI Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney
2007	J	`jnl`	Fast Population Coding. Neural Comput. Quentin J. M. Huys, Richard S. Zemel, Rama Natarajan, Peter Dayan
2006	—	`conf`	Automated Detection of Unusual Events on Stairs. CRV Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel
2006	A*	`conf`	Combining discriminative features to infer complex trajectories. ICML David A. Ross, Simon Osindero, Richard S. Zemel
2006	J	`jnl`	Learning Parts-Based Representations of Data. J. Mach. Learn. Res. David A. Ross, Richard S. Zemel
2006	—	`conf`	Learning and Incorporating Top-Down Cues in Image Segmentation. ECCV (1) Xuming He, Richard S. Zemel, Debajyoti Ray
2006	J	`jnl`	Topological map learning from outdoor image sequences. J. Field Robotics Xuming He, Richard S. Zemel, Volodymyr Mnih
2005	A	`conf`	Unsupervised Learning with Non-Ignorable Missing Data. AISTATS Benjamin M. Marlin, Sam T. Roweis, Richard S. Zemel
2004	—	`conf`	Multiscale Conditional Random Fields for Image Labeling. CVPR (2) Xuming He, Richard S. Zemel, Miguel Á. Carreira-Perpiñán
2004	—	`conf`	Probabilistic Computation in Spiking Populations. NIPS Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan
2004	J	`jnl`	Probabilistic sequential independent components analysis. IEEE Trans. Neural Networks Max Welling, Richard S. Zemel, Geoffrey E. Hinton
2004	—	`conf`	Proximity Graphs for Clustering and Manifold Learning. NIPS Miguel Á. Carreira-Perpiñán, Richard S. Zemel
2004	A*	`conf`	The multiple multiplicative factor model for collaborative filtering. ICML Benjamin M. Marlin, Richard S. Zemel
2003	A	`conf`	Active Collaborative Filtering. UAI Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin
2003	A	`conf`	An Active Approach to Collaborative Filtering. AISTATS Richard S. Zemel, Craig Boutilier
2003	—	`ch.`	Cortical Belief Networks. Computational Models for Neuroscience Richard S. Zemel
2003	A	`conf`	Efficient Parametric Projection Pursuit Density Estimation. UAI Max Welling, Richard S. Zemel, Geoffrey E. Hinton
2002	—	`conf`	Multiple Cause Vector Quantization. NIPS David A. Ross, Richard S. Zemel
2002	—	`conf`	Self Supervised Boosting. NIPS Max Welling, Richard S. Zemel, Geoffrey E. Hinton
2001	J	`jnl`	Localist Attractor Networks. Neural Comput. Richard S. Zemel, Michael Mozer
2000	—	`conf`	A Gradient-Based Boosting Algorithm for Regression Problems. NIPS Richard S. Zemel, Toniann Pitassi
2000	J	`jnl`	Encoding multiple orientations in a recurrent network. Neurocomputing Richard S. Zemel, Jonathan W. Pillow
1999	—	`conf`	A Generative Model for Attractor Dynamics. NIPS Richard S. Zemel, Michael Mozer
1999	—	`conf`	Managing Uncertainty in Cue Combination. NIPS Zhiyong Yang, Richard S. Zemel
1998	—	`conf`	Distributional Population Codes and Multiple Motion Models. NIPS Richard S. Zemel, Peter Dayan
1998	J	`jnl`	Probabilistic Interpretation of Population Codes. Neural Comput. Richard S. Zemel, Peter Dayan, Alexandre Pouget
1997	—	`conf`	Combining Probabilistic Population Codes. IJCAI Richard S. Zemel, Peter Dayan
1996	—	`conf`	Probabilistic Interpretation of Population Codes. NIPS Richard S. Zemel, Peter Dayan, Alexandre Pouget
1996	—	`conf`	Selective Integration: A Model for Disparity Estimation. NIPS Michael S. Gray, Alexandre Pouget, Richard S. Zemel, Steven J. Nowlan, Terrence J. Sejnowski
1995	J	`jnl`	Competition and Multiple Cause Models. Neural Comput. Peter Dayan, Richard S. Zemel
1995	J	`jnl`	Learning Population Codes by Minimizing Description Length. Neural Comput. Richard S. Zemel, Geoffrey E. Hinton
1995	J	`jnl`	Lending direction to neural networks. Neural Networks Richard S. Zemel, Christopher K. I. Williams, Michael Mozer
1995	J	`jnl`	The Helmholtz machine. Neural Comput. Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel
1994	—	`—`	A minimum description length framework for unsupervised learning. Richard S. Zemel
1994	—	`conf`	Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex. NIPS Richard S. Zemel, Terrence J. Sejnowski
1993	—	`conf`	Autoencoders, Minimum Description Length and Helmholtz Free Energy. NIPS Geoffrey E. Hinton, Richard S. Zemel
1993	—	`conf`	Developing Population Codes by Minimizing Description Length. NIPS Richard S. Zemel, Geoffrey E. Hinton
1992	—	`conf`	Directional-Unit Boltzmann Machines. NIPS Richard S. Zemel, Christopher K. I. Williams, Michael Mozer
1992	J	`jnl`	Learning to Segment Images Using Dynamic Feature Binding. Neural Comput. Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams
1991	—	`conf`	Learning to Segment Images Using Dynamic Feature Binding. NIPS Michael Mozer, Richard S. Zemel, Marlene Behrmann
1990	—	`conf`	Discovering Viewpoint-Invariant Relationships That Characterize Objects. NIPS Richard S. Zemel, Geoffrey E. Hinton
1989	—	`conf`	TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton