Richard S. Zemel
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
97
Venues
21
Active years
1997–2025
Best venue rank
A*
Where they publish
Papers
97 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ACL | Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation. | Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris |
| 2025 | ICML | QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. | Zhun Deng, Thomas P. Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard S. Zemel |
| 2025 | ICML | Adaptive Elicitation of Latent Information Using Natural Language. | Jimmy Wang, Thomas P. Zollo, Richard S. Zemel, Hongseok Namkoong |
| 2024 | ECCV | Controlling the World by Sleight of Hand. | Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick, Carl Vondrick, Richard S. Zemel |
| 2024 | EMNLP | Training-free Deep Concept Injection Enables Language Models for Video Question Answering. | Xudong Lin, Manling Li, Richard S. Zemel, Heng Ji, Shih-Fu Chang |
| 2024 | EMNLP | FLIRT: Feedback Loop In-context Red Teaming. | Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard S. Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta |
| 2024 | EMNLP | Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification. | Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard S. Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris |
| 2024 | EMNLP | Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities. | Sachit Menon, Richard S. Zemel, Carl Vondrick |
| 2024 | ICLR | 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 S. Zemel |
| 2024 | ICML | Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. | Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel |
| 2024 | NAACL | The steerability of large language models toward data-driven personas. | Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta |
| 2024 | NAACL | Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies. | Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta |
| 2024 | NAACL | Toward Informal Language Processing: Knowledge of Slang in Large Language Models. | Zhewei Sun, Qian Hu, Rahul Gupta, Richard S. Zemel, Yang Xu |
| 2023 | ACL | Resolving Ambiguities in Text-to-Image Generative Models. | Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Rahul Gupta |
| 2023 | EMNLP | Coordinated Replay Sample Selection for Continual Federated Learning. | Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard S. Zemel, Rahul Gupta |
| 2023 | ICCV | SurfsUp: Learning Fluid Simulation for Novel Surfaces. | Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel |
| 2023 | ICLR | Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions. | Jake Snell, Thomas P. Zollo, Zhun Deng, Toniann Pitassi, Richard S. Zemel |
| 2023 | WSDM | Incorporating Fairness in Large Scale NLU Systems. | 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 | WSDM | Privacy in the Time of Language Models. | Charith Peris, Christophe Dupuy, Jimit Majmudar, Rahil Parikh, Sami Smaili, Richard S. Zemel, Rahul Gupta |
| 2022 | NAACL | Semantically Informed Slang Interpretation. | Zhewei Sun, Richard S. Zemel, Yang Xu |
| 2021 | ICLR | A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. | Renjie Liao, Raquel Urtasun, Richard S. Zemel |
| 2021 | ICLR | Theoretical bounds on estimation error for meta-learning. | James Lucas, Mengye Ren, Irene Raissa Kameni, Toniann Pitassi, Richard S. Zemel |
| 2021 | ICLR | Wandering within a world: Online contextualized few-shot learning. | Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard S. Zemel |
| 2021 | ICLR | Bayesian Few-Shot Classification with One-vs-Each Plya-Gamma Augmented Gaussian Processes. | Jake Snell, Richard S. Zemel |
| 2021 | ICML | Environment Inference for Invariant Learning. | Elliot Creager, Jrn-Henrik Jacobsen, Richard S. Zemel |
| 2021 | ICML | On Monotonic Linear Interpolation of Neural Network Parameters. | James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse |
| 2021 | ICML | Learning a Universal Template for Few-shot Dataset Generalization. | Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin |
| 2021 | ICML | SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. | Alexander Wang, Mengye Ren, Richard S. Zemel |
| 2021 | UAI | NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. | Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao |
| 2020 | ICLR | Understanding the Limitations of Conditional Generative Models. | Ethan Fetaya, Jrn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel |
| 2020 | ICML | Causal Modeling for Fairness In Dynamical Systems. | Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel |
| 2020 | ICML | Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. | Will Grathwohl, Kuan-Chieh Wang, Jrn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel |
| 2020 | ICML | Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. | Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier |
| 2019 | ACSSC | Inference in Probabilistic Graphical Models by Graph Neural Networks. | KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow |
| 2019 | CogSci | Slang Generation as Categorization. | Zhewei Sun, Richard S. Zemel, Yang Xu |
| 2019 | CoRL | A Divergence Minimization Perspective on Imitation Learning Methods. | Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu |
| 2019 | ICLR | Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning. | Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel |
| 2019 | ICLR | Excessive Invariance Causes Adversarial Vulnerability. | Jrn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge |
| 2019 | ICLR | Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. | Marc T. Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S. Zemel |
| 2019 | ICLR | LanczosNet: Multi-Scale Deep Graph Convolutional Networks. | Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel |
| 2019 | ICLR | Aggregated Momentum: Stability Through Passive Damping. | James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse |
| 2019 | ICML | Understanding the Origins of Bias in Word Embeddings. | Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel |
| 2019 | ICML | Flexibly Fair Representation Learning by Disentanglement. | Elliot Creager, David Madras, Jrn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel |
| 2019 | ICML | Lorentzian Distance Learning for Hyperbolic Representations. | Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel |
| 2018 | ICLR | Gradient-based Optimization of Neural Network Architecture. | Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel |
| 2018 | ICLR | Graph Partition Neural Networks for Semi-Supervised Classification. | Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel |
| 2018 | ICLR | Predict Responsibly: Increasing Fairness by Learning to Defer. | David Madras, Toniann Pitassi, Richard S. Zemel |
| 2018 | ICLR | 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 |
| 2018 | ICLR | Inference in probabilistic graphical models by Graph Neural Networks. | KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow |
| 2018 | ICLR | Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. | Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel |
| 2018 | ICML | Neural Relational Inference for Interacting Systems. | Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel |
| 2018 | ICML | Reviving and Improving Recurrent Back-Propagation. | Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel |
| 2018 | ICML | Learning Adversarially Fair and Transferable Representations. | David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel |
| 2018 | ICML | Adversarial Distillation of Bayesian Neural Network Posteriors. | Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel |
| 2017 | CVPR | Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. | Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing |
| 2017 | CVPR | End-to-End Instance Segmentation with Recurrent Attention. | Mengye Ren, Richard S. Zemel |
| 2017 | ICIP | Learning to generate images with perceptual similarity metrics. | Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel |
| 2017 | ICLR | Joint Embeddings of Scene Graphs and Images. | Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel |
| 2017 | ICLR | Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. | Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel |
| 2017 | ICML | Deep Spectral Clustering Learning. | Marc T. Law, Raquel Urtasun, Richard S. Zemel |
| 2017 | UAI | Stochastic Segmentation Trees for Multiple Ground Truths. | Jake Snell, Richard S. Zemel |
| 2016 | ICMI | Learning to generate images and their descriptions (keynote). | Richard S. Zemel |
| 2016 | ICML | Training Deep Neural Networks via Direct Loss Minimization. | Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun |
| 2015 | ICCV | Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books. | Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler |
| 2015 | ICML | Generative Moment Matching Networks. | Yujia Li, Kevin Swersky, Richard S. Zemel |
| 2015 | ICML | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. | Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio |
| 2014 | ICML | Multimodal Neural Language Models. | Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel |
| 2014 | ICML | High Order Regularization for Semi-Supervised Learning of Structured Output Problems. | Yujia Li, Richard S. Zemel |
| 2014 | ICML | Input Warping for Bayesian Optimization of Non-Stationary Functions. | Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams |
| 2014 | KDD | Leveraging user libraries to bootstrap collaborative filtering. | Laurent Charlin, Richard S. Zemel, Hugo Larochelle |
| 2013 | CIKM | CRF framework for supervised preference aggregation. | Maksims Volkovs, Richard S. Zemel |
| 2013 | CVPR | Exploring Compositional High Order Pattern Potentials for Structured Output Learning. | Yujia Li, Daniel Tarlow, Richard S. Zemel |
| 2013 | ICML | Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. | Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel |
| 2013 | ICML | Learning Fair Representations. | Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork |
| 2012 | CIKM | Learning to rank by aggregating expert preferences. | Maksims Volkovs, Hugo Larochelle, Richard S. Zemel |
| 2012 | ICML | Active Learning for Matching Problems. | Laurent Charlin, Richard S. Zemel, Craig Boutilier |
| 2012 | WWW | A flexible generative model for preference aggregation. | Maksims Volkovs, Richard S. Zemel |
| 2012 | UAI | Fast Exact Inference for Recursive Cardinality Models. | Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey |
| 2011 | IJCAI | Recommender Systems, Missing Data and Statistical Model Estimation. | Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney |
| 2011 | UAI | A Framework for Optimizing Paper Matching. | Laurent Charlin, Richard S. Zemel, Craig Boutilier |
| 2011 | UAI | Graph Cuts is a Max-Product Algorithm. | Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey |
| 2009 | ICML | BoltzRank: learning to maximize expected ranking gain. | Maksims Volkovs, Richard S. Zemel |
| 2009 | RecSys | Collaborative prediction and ranking with non-random missing data. | Benjamin M. Marlin, Richard S. Zemel |
| 2008 | CVPR | Latent topic random fields: Learning using a taxonomy of labels. | Xuming He, Richard S. Zemel |
| 2008 | CVPR | Learning stick-figure models using nonparametric Bayesian priors over trees. | Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis |
| 2008 | ECCV | Unsupervised Learning of Skeletons from Motion. | David A. Ross, Daniel Tarlow, Richard S. Zemel |
| 2008 | UAI | Flexible Priors for Exemplar-based Clustering. | Daniel Tarlow, Richard S. Zemel, Brendan J. Frey |
| 2007 | UAI | Collaborative Filtering and the Missing at Random Assumption. | Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney |
| 2006 | ECCV | Learning and Incorporating Top-Down Cues in Image Segmentation. | Xuming He, Richard S. Zemel, Debajyoti Ray |
| 2006 | ICML | Combining discriminative features to infer complex trajectories. | David A. Ross, Simon Osindero, Richard S. Zemel |
| 2005 | AISTATS | Unsupervised Learning with Non-Ignorable Missing Data. | Benjamin M. Marlin, Sam T. Roweis, Richard S. Zemel |
| 2004 | CVPR | Multiscale Conditional Random Fields for Image Labeling. | Xuming He, Richard S. Zemel, Miguel . Carreira-Perpin |
| 2004 | ICML | The multiple multiplicative factor model for collaborative filtering. | Benjamin M. Marlin, Richard S. Zemel |
| 2003 | AISTATS | An Active Approach to Collaborative Filtering. | Richard S. Zemel, Craig Boutilier |
| 2003 | UAI | Active Collaborative Filtering. | Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin |
| 2003 | UAI | Efficient Parametric Projection Pursuit Density Estimation. | Max Welling, Richard S. Zemel, Geoffrey E. Hinton |
| 1997 | IJCAI | Combining Probabilistic Population Codes. | Richard S. Zemel, Peter Dayan |