default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDRichard S. Zemel
Person information
- affiliation: Columbia University, New York, NY, USA
- affiliation: University of Toronto, ON, Canada
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c148]Thomas P. Zollo, Todd Morrill, Zhun Deng, Jake Snell, Toniann Pitassi, Richard S. Zemel:
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models. ICLR 2024- [c147]Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel:
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. ICML 2024 - [c146]Zhewei Sun, Qian Hu, Rahul Gupta, Richard S. Zemel, Yang Xu:
Toward Informal Language Processing: Knowledge of Slang in Large Language Models. NAACL-HLT 2024: 1683-1701 - [c145]Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard S. Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta:
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies. NAACL-HLT (Findings) 2024: 1739-1756 - [c144]Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard S. Zemel, Rahul Gupta:
The steerability of large language models toward data-driven personas. NAACL-HLT 2024: 7290-7305 - [i98]Elliot Creager, Richard S. Zemel:
Online Algorithmic Recourse by Collective Action. CoRR abs/2401.00055 (2024) - [i97]Tiantian Feng, Anil Ramakrishna, Jimit Majmudar, Charith Peris, Jixuan Wang, Clement Chung, Richard S. Zemel, Morteza Ziyadi, Rahul Gupta:
Partial Federated Learning. CoRR abs/2403.01615 (2024) - [i96]Zhewei Sun, Qian Hu, Rahul Gupta, Richard S. Zemel, Yang Xu:
Toward Informal Language Processing: Knowledge of Slang in Large Language Models. CoRR abs/2404.02323 (2024) - [i95]Yipeng Zhang, Laurent Charlin, Richard S. Zemel, Mengye Ren:
Integrating Present and Past in Unsupervised Continual Learning. CoRR abs/2404.19132 (2024) - [i94]Sachit Menon, Richard S. Zemel, Carl Vondrick:
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities. CoRR abs/2406.14562 (2024) - [i93]Sruthi Sudhakar, Ruoshi Liu, Basile Van Hoorick, Carl Vondrick, Richard S. Zemel:
Controlling the World by Sleight of Hand. CoRR abs/2408.07147 (2024) - 2023
- [c143]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: 14367-14388 - [c142]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: 331-342 - [c141]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: 1246-1266 - [c140]Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel:
SurfsUp: Learning Fluid Simulation for Novel Surfaces. ICCV 2023: 14179-14189 - [c139]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 - [c138]Zhun Deng, Thomas P. Zollo, Jake Snell, Toniann Pitassi, Richard S. Zemel:
Distribution-Free Statistical Dispersion Control for Societal Applications. NeurIPS 2023 - [c137]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: 1289-1290 - [c136]Charith Peris, Christophe Dupuy, Jimit Majmudar, Rahil Parikh, Sami Smaili, Richard S. Zemel, Rahul Gupta:
Privacy in the Time of Language Models. WSDM 2023: 1291-1292 - [i92]Arjun Mani, Ishaan Preetam Chandratreya, Elliot Creager, Carl Vondrick, Richard S. Zemel:
SURFSUP: Learning Fluid Simulation for Novel Surfaces. CoRR abs/2304.06197 (2023) - [i91]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 abs/2305.09941 (2023) - [i90]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 abs/2308.04265 (2023) - [i89]Zhun Deng, Thomas P. Zollo, Jake C. Snell, Toniann Pitassi, Richard S. Zemel:
Distribution-Free Statistical Dispersion Control for Societal Applications. CoRR abs/2309.13786 (2023) - [i88]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 abs/2310.15054 (2023) - [i87]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 abs/2311.04978 (2023) - [i86]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 abs/2311.09473 (2023) - [i85]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 abs/2311.13628 (2023) - [i84]Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel:
ICL Markup: Structuring In-Context Learning using Soft-Token Tags. CoRR abs/2312.07405 (2023) - [i83]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 abs/2312.11779 (2023) - [i82]Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel:
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. CoRR abs/2312.17463 (2023) - 2022
- [j24]Lachlan McCalman, Daniel Steinberg, Grace Abuhamad, Marc-Etienne Brunet, Robert C. Williamson, Richard S. Zemel:
Assessing AI Fairness in Finance. Computer 55(1): 94-97 (2022) - [c135]Sindy Löwe, David Madras, Richard S. Zemel, Max Welling:
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CLeaR 2022: 509-525 - [c134]Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge:
Disentanglement and Generalization Under Correlation Shifts. CoLLAs 2022: 116-141 - [c133]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: 90-106 - [c132]Zhewei Sun, Richard S. Zemel, Yang Xu:
Semantically Informed Slang Interpretation. NAACL-HLT 2022: 5213-5231 - [c131]Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham:
Deep Ensembles Work, But Are They Necessary? NeurIPS 2022 - [c130]Marc-Etienne Brunet, Ashton Anderson, Richard S. Zemel:
Implications of Model Indeterminacy for Explanations of Automated Decisions. NeurIPS 2022 - [i81]Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani:
Variational Model Inversion Attacks. CoRR abs/2201.10787 (2022) - [i80]Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham:
Deep Ensembles Work, But Are They Necessary? CoRR abs/2202.06985 (2022) - [i79]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 abs/2204.03558 (2022) - [i78]Zhewei Sun, Richard S. Zemel, Yang Xu:
Semantically Informed Slang Interpretation. CoRR abs/2205.00616 (2022) - [i77]Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard S. Zemel:
Differentially Private Decoding in Large Language Models. CoRR abs/2205.13621 (2022) - [i76]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 abs/2211.12503 (2022) - [i75]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 abs/2212.13629 (2022) - 2021
- [j23]Zhewei Sun, Richard S. Zemel, Yang Xu:
A Computational Framework for Slang Generation. Trans. Assoc. Comput. Linguistics 9: 462-478 (2021) - [c129]Renjie Liao, Raquel Urtasun, Richard S. Zemel:
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. ICLR 2021 - [c128]James Lucas, Mengye Ren, Irene Raissa Kameni, Toniann Pitassi, Richard S. Zemel:
Theoretical bounds on estimation error for meta-learning. ICLR 2021 - [c127]Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, Richard S. Zemel:
Wandering within a world: Online contextualized few-shot learning. ICLR 2021 - [c126]Jake Snell, Richard S. Zemel:
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. ICLR 2021 - [c125]Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel:
Environment Inference for Invariant Learning. ICML 2021: 2189-2200 - [c124]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: 7168-7179 - [c123]Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin:
Learning a Universal Template for Few-shot Dataset Generalization. ICML 2021: 10424-10433 - [c122]Alexander Wang, Mengye Ren, Richard S. Zemel:
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. ICML 2021: 10870-10881 - [c121]Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard S. Zemel, Alireza Makhzani:
Variational Model Inversion Attacks. NeurIPS 2021: 9706-9719 - [c120]David Madras, Richard S. Zemel:
Identifying and Benchmarking Natural Out-of-Context Prediction Problems. NeurIPS 2021: 15344-15358 - [c119]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: 1089-1099 - [e3]Madeleine Clare Elish, William Isaac, Richard S. Zemel:
FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021. ACM 2021, ISBN 978-1-4503-8309-7 [contents] - [i74]Zhewei Sun, Richard S. Zemel, Yang Xu:
A Computational Framework for Slang Generation. CoRR abs/2102.01826 (2021) - [i73]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 abs/2104.11044 (2021) - [i72]Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin:
Learning a Universal Template for Few-shot Dataset Generalization. CoRR abs/2105.07029 (2021) - [i71]Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao:
NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation. CoRR abs/2106.13435 (2021) - [i70]Jacob Kelly, Richard S. Zemel, Will Grathwohl:
Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data. CoRR abs/2108.04227 (2021) - [i69]Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel:
Online Unsupervised Learning of Visual Representations and Categories. CoRR abs/2109.05675 (2021) - [i68]David Madras, Richard S. Zemel:
Identifying and Benchmarking Natural Out-of-Context Prediction Problems. CoRR abs/2110.13223 (2021) - [i67]Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard S. Zemel, Matthias Bethge:
Disentanglement and Generalization Under Correlation Shifts. CoRR abs/2112.14754 (2021) - 2020
- [j22]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): 665-673 (2020) - [c118]Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl, Richard S. Zemel:
Understanding the Limitations of Conditional Generative Models. ICLR 2020 - [c117]Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel:
Causal Modeling for Fairness In Dynamical Systems. ICML 2020: 2185-2195 - [c116]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: 3732-3747 - [c115]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: 6987-6998 - [i66]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 abs/2002.05616 (2020) - [i65]Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann:
Shortcut Learning in Deep Neural Networks. CoRR abs/2004.07780 (2020) - [i64]Sindy Löwe, David Madras, Richard S. Zemel, Max Welling:
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CoRR abs/2006.10833 (2020) - [i63]Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel:
Wandering Within a World: Online Contextualized Few-Shot Learning. CoRR abs/2007.04546 (2020) - [i62]Jake Snell, Richard S. Zemel:
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes. CoRR abs/2007.10417 (2020) - [i61]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 abs/2008.00104 (2020) - [i60]Alexander Wang, Mengye Ren, Richard S. Zemel:
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings. CoRR abs/2009.04806 (2020) - [i59]James Lucas, Mengye Ren, Irene Kameni, Toniann Pitassi, Richard S. Zemel:
Theoretical bounds on estimation error for meta-learning. CoRR abs/2010.07140 (2020) - [i58]Elliot Creager, Jörn-Henrik Jacobsen, Richard S. Zemel:
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization. CoRR abs/2010.07249 (2020) - [i57]Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel:
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. CoRR abs/2011.06485 (2020) - [i56]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 abs/2012.05895 (2020) - [i55]Renjie Liao, Raquel Urtasun, Richard S. Zemel:
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. CoRR abs/2012.07690 (2020)
2010 – 2019
- 2019
- [c114]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: 868-875 - [c113]Zhewei Sun, Richard S. Zemel, Yang Xu:
Slang Generation as Categorization. CogSci 2019: 2898-2904 - [c112]Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu:
A Divergence Minimization Perspective on Imitation Learning Methods. CoRL 2019: 1259-1277 - [c111]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. FAT 2019: 349-358 - [c110]Seyed Kamyar Seyed Ghasemipour, Shane Gu, Richard S. Zemel:
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning. DGS@ICLR 2019 - [c109]Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge:
Excessive Invariance Causes Adversarial Vulnerability. ICLR (Poster) 2019 - [c108]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 - [c107]Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel:
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR (Poster) 2019 - [c106]James Lucas, Shengyang Sun, Richard S. Zemel, Roger B. Grosse:
Aggregated Momentum: Stability Through Passive Damping. ICLR (Poster) 2019 - [c105]Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel:
Understanding the Origins of Bias in Word Embeddings. ICML 2019: 803-811 - [c104]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: 1436-1445 - [c103]Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel:
Lorentzian Distance Learning for Hyperbolic Representations. ICML 2019: 3672-3681 - [c102]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: 4257-4267 - [c101]Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. NeurIPS 2019: 5276-5286 - [c100]Seyed Kamyar Seyed Ghasemipour, Shixiang Gu, Richard S. Zemel:
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies. NeurIPS 2019: 7879-7889 - [i54]Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel:
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. CoRR abs/1901.01484 (2019) - [i53]Amir Rosenfeld, Richard S. Zemel, John K. Tsotsos:
High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks. CoRR abs/1903.10920 (2019) - [i52]Ethan Fetaya, Jörn-Henrik Jacobsen, Richard S. Zemel:
Conditional Generative Models are not Robust. CoRR abs/1906.01171 (2019) - [i51]Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. CoRR abs/1906.02589 (2019) - [i50]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 abs/1906.09427 (2019) - [i49]Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel:
Causal Modeling for Fairness in Dynamical Systems. CoRR abs/1909.09141 (2019) - [i48]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 abs/1910.00760 (2019) - [i47]Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel, Shixiang Gu:
A Divergence Minimization Perspective on Imitation Learning Methods. CoRR abs/1911.02256 (2019) - 2018
- [c99]Will Grathwohl, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, Richard S. Zemel:
Gradient-based Optimization of Neural Network Architecture. ICLR (Workshop) 2018 - [c98]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 - [c97]David Madras, Toniann Pitassi, Richard S. Zemel:
Predict Responsibly: Increasing Fairness by Learning to Defer. ICLR (Workshop) 2018 - [c96]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. ICLR (Poster) 2018 - [c95]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 - [c94]Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel:
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. ICLR (Workshop) 2018 - [c93]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. ICML 2018: 2693-2702 - [c92]Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel:
Reviving and Improving Recurrent Back-Propagation. ICML 2018: 3088-3097 - [c91]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Learning Adversarially Fair and Transferable Representations. ICML 2018: 3381-3390 - [c90]Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel:
Adversarial Distillation of Bayesian Neural Network Posteriors. ICML 2018: 5177-5186 - [c89]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: 1744-1753 - [c88]David Madras, Toniann Pitassi, Richard S. Zemel:
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. NeurIPS 2018: 6150-6160 - [c87]Jack Klys, Jake Snell, Richard S. Zemel:
Learning Latent Subspaces in Variational Autoencoders. NeurIPS 2018: 6445-6455 - [i46]Thomas N. Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard S. Zemel:
Neural Relational Inference for Interacting Systems. CoRR abs/1802.04687 (2018) - [i45]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Learning Adversarially Fair and Transferable Representations. CoRR abs/1802.06309 (2018) - [i44]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 abs/1803.00676 (2018) - [i43]Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel:
Graph Partition Neural Networks for Semi-Supervised Classification. CoRR abs/1803.06272 (2018) - [i42]