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ORCIDRichard S. Zemel
Person information
- affiliation: Columbia University, New York, NY, USA
- affiliation: University of Toronto, ON, Canada
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Books and Theses
- 1994
[b1]Richard S. Zemel:
A minimum description length framework for unsupervised learning. University of Toronto, Canada, 1994
Journal Articles
- 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) - 2021
- [j23]Zhewei Sun, Richard S. Zemel, Yang Xu:
A Computational Framework for Slang Generation. Trans. Assoc. Comput. Linguistics 9: 462-478 (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) - 2015
- [j21]Yin Zheng, Richard S. Zemel, Yu-Jin Zhang, Hugo Larochelle:
A Neural Autoregressive Approach to Attention-based Recognition. Int. J. Comput. Vis. 113(1): 67-79 (2015) - [j20]Karteek Alahari, Dhruv Batra, Srikumar Ramalingam, Nikos Paragios, Richard S. Zemel:
Guest Editors' Introduction: Special Section on Higher Order Graphical Models in Computer Vision. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1321-1322 (2015) - 2014
- [j19]Maksims Volkovs, Richard S. Zemel:
New learning methods for supervised and unsupervised preference aggregation. J. Mach. Learn. Res. 15(1): 1135-1176 (2014) - 2010
- [j18]David A. Ross, Daniel Tarlow, Richard S. Zemel:
Learning Articulated Structure and Motion. Int. J. Comput. Vis. 88(2): 214-237 (2010) - [j17]Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. Strother:
Comparing Classification Methods for Longitudinal fMRI Studies. Neural Comput. 22(11): 2729-2762 (2010) - 2009
- [j16]Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel, Alex Mihailidis:
Automated detection of unusual events on stairs. Image Vis. Comput. 27(1-2): 153-166 (2009) - 2008
- [j15]F. Klam, Richard S. Zemel, Alexandre Pouget:
Population Coding with Motion Energy Filters: The Impact of Correlations. Neural Comput. 20(1): 146-175 (2008) - [j14]Rama Natarajan, Quentin J. M. Huys, Peter Dayan, Richard S. Zemel:
Encoding and Decoding Spikes for Dynamic Stimuli. Neural Comput. 20(9): 2325-2360 (2008) - [j13]Liam Stewart, Xuming He, Richard S. Zemel:
Learning Flexible Features for Conditional Random Fields. IEEE Trans. Pattern Anal. Mach. Intell. 30(8): 1415-1426 (2008) - 2007
- [j12]Quentin J. M. Huys, Richard S. Zemel, Rama Natarajan, Peter Dayan:
Fast Population Coding. Neural Comput. 19(2): 404-441 (2007) - 2006
- [j11]Xuming He, Richard S. Zemel, Volodymyr Mnih:
Topological map learning from outdoor image sequences. J. Field Robotics 23(11-12): 1091-1104 (2006) - [j10]David A. Ross, Richard S. Zemel:
Learning Parts-Based Representations of Data. J. Mach. Learn. Res. 7: 2369-2397 (2006) - 2004
- [j9]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Probabilistic sequential independent components analysis. IEEE Trans. Neural Networks 15(4): 838-849 (2004) - 2001
- [j8]Richard S. Zemel, Michael Mozer:
Localist Attractor Networks. Neural Comput. 13(5): 1045-1064 (2001) - 2000
- [j7]Richard S. Zemel, Jonathan W. Pillow:
Encoding multiple orientations in a recurrent network. Neurocomputing 32-33: 609-616 (2000) - 1998
- [j6]Richard S. Zemel, Peter Dayan, Alexandre Pouget:
Probabilistic Interpretation of Population Codes. Neural Comput. 10(2): 403-430 (1998) - 1995
- [j5]Richard S. Zemel, Geoffrey E. Hinton:
Learning Population Codes by Minimizing Description Length. Neural Comput. 7(3): 549-564 (1995) - [j4]Peter Dayan, Richard S. Zemel:
Competition and Multiple Cause Models. Neural Comput. 7(3): 565-579 (1995) - [j3]Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel:
The Helmholtz machine. Neural Comput. 7(5): 889-904 (1995) - [j2]Richard S. Zemel, Christopher K. I. Williams, Michael Mozer:
Lending direction to neural networks. Neural Networks 8(4): 503-512 (1995) - 1992
- [j1]Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams:
Learning to Segment Images Using Dynamic Feature Binding. Neural Comput. 4(5): 650-665 (1992)
Conference and Workshop Papers
- 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 - 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 - 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 - 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 - 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 - 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 - 2017
- [c86]Mengye Ren, Richard S. Zemel:
End-to-End Instance Segmentation with Recurrent Attention. CVPR 2017: 293-301 - [c85]Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing:
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. CVPR 2017: 5948-5956 - [c84]Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel:
Learning to generate images with perceptual similarity metrics. ICIP 2017: 4277-4281 - [c83]Eugene Belilovsky, Matthew B. Blaschko, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel:
Joint Embeddings of Scene Graphs and Images. ICLR (Workshop) 2017 - [c82]Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel:
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. ICLR (Poster) 2017 - [c81]Marc T. Law, Raquel Urtasun, Richard S. Zemel:
Deep Spectral Clustering Learning. ICML 2017: 1985-1994 - [c80]Eleni Triantafillou, Richard S. Zemel, Raquel Urtasun:
Few-Shot Learning Through an Information Retrieval Lens. NIPS 2017: 2255-2265 - [c79]Jake Snell, Kevin Swersky, Richard S. Zemel:
Prototypical Networks for Few-shot Learning. NIPS 2017: 4077-4087 - [c78]Yujia Li, Alexander G. Schwing, Kuan-Chieh Wang, Richard S. Zemel:
Dualing GANs. NIPS 2017: 5606-5616 - [c77]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. NIPS 2017: 6446-6456 - [c76]Jake Snell, Richard S. Zemel:
Stochastic Segmentation Trees for Multiple Ground Truths. UAI 2017 - 2016
- [c75]Richard S. Zemel:
Learning to generate images and their descriptions (keynote). ICMI 2016: 2 - [c74]Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Training Deep Neural Networks via Direct Loss Minimization. ICML 2016: 2169-2177 - [c73]Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel:
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. NIPS 2016: 4898-4906 - [c72]Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Learning Deep Parsimonious Representations. NIPS 2016: 5076-5084 - [c71]Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel:
Towards Generalizable Sentence Embeddings. Rep4NLP@ACL 2016: 239-248 - [c70]Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard S. Zemel:
Gated Graph Sequence Neural Networks. ICLR (Poster) 2016 - [c69]Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, Richard S. Zemel:
The Variational Fair Autoencoder. ICLR 2016 - 2015
- [c68]Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler:
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books. ICCV 2015: 19-27 - [c67]Yujia Li, Kevin Swersky, Richard S. Zemel:
Generative Moment Matching Networks. ICML 2015: 1718-1727 - [c66]Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio:
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. ICML 2015: 2048-2057 - [c65]Mengye Ren, Ryan Kiros, Richard S. Zemel:
Exploring Models and Data for Image Question Answering. NIPS 2015: 2953-2961 - [c64]Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Raquel Urtasun, Antonio Torralba, Sanja Fidler:
Skip-Thought Vectors. NIPS 2015: 3294-3302 - 2014
- [c63]Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel:
Multimodal Neural Language Models. ICML 2014: 595-603 - [c62]Yujia Li, Richard S. Zemel:
High Order Regularization for Semi-Supervised Learning of Structured Output Problems. ICML 2014: 1368-1376 - [c61]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML 2014: 1674-1682 - [c60]Laurent Charlin, Richard S. Zemel, Hugo Larochelle:
Leveraging user libraries to bootstrap collaborative filtering. KDD 2014: 173-182 - [c59]Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov:
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. NIPS 2014: 2348-2356 - 2013
- [c58]Maksims Volkovs, Richard S. Zemel:
CRF framework for supervised preference aggregation. CIKM 2013: 89-98 - [c57]Yujia Li, Daniel Tarlow, Richard S. Zemel:
Exploring Compositional High Order Pattern Potentials for Structured Output Learning. CVPR 2013: 49-56 - [c56]Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel:
Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) 2013: 199-207 - [c55]Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork:
Learning Fair Representations. ICML (3) 2013: 325-333 - [c54]Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams:
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. NIPS 2013: 1932-1940 - [c53]James Martens, Arkadev Chattopadhyay, Toniann Pitassi, Richard S. Zemel:
On the Expressive Power of Restricted Boltzmann Machines. NIPS 2013: 2877-2885 - 2012
- [c52]Maksims Volkovs, Hugo Larochelle, Richard S. Zemel:
Learning to rank by aggregating expert preferences. CIKM 2012: 843-851 - [c51]Laurent Charlin, Richard S. Zemel, Craig Boutilier:
Active Learning for Matching Problems. ICML 2012 - [c50]Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel:
Fairness through awareness. ITCS 2012: 214-226 - [c49]Maksims Volkovs, Richard S. Zemel:
Efficient Sampling for Bipartite Matching Problems. NIPS 2012: 1322-1330 - [c48]Maksims Volkovs, Richard S. Zemel:
Collaborative Ranking With 17 Parameters. NIPS 2012: 2303-2311 - [c47]Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey:
Probabilistic n-Choose-k Models for Classification and Ranking. NIPS 2012: 3059-3067 - [c46]Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams:
Cardinality Restricted Boltzmann Machines. NIPS 2012: 3302-3310 - [c45]Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey:
Fast Exact Inference for Recursive Cardinality Models. UAI 2012: 825-834 - [c44]Maksims Volkovs, Richard S. Zemel:
A flexible generative model for preference aggregation. WWW 2012: 479-488 - [c43]Daniel Tarlow, Richard S. Zemel:
Structured Output Learning with High Order Loss Functions. AISTATS 2012: 1212-1220 - [c42]Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel:
Randomized Optimum Models for Structured Prediction. AISTATS 2012: 1221-1229 - 2011
- [c41]Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Recommender Systems, Missing Data and Statistical Model Estimation. IJCAI 2011: 2686-2691 - [c40]Laurent Charlin, Richard S. Zemel, Craig Boutilier:
A Framework for Optimizing Paper Matching. UAI 2011: 86-95 - [c39]Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey:
Graph Cuts is a Max-Product Algorithm. UAI 2011: 671-680 - 2010
- [c38]Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel:
HOP-MAP: Efficient Message Passing with High Order Potentials. AISTATS 2010: 812-819 - 2009
- [c37]Maksims Volkovs, Richard S. Zemel:
BoltzRank: learning to maximize expected ranking gain. ICML 2009: 1089-1096 - [c36]Benjamin M. Marlin, Richard S. Zemel:
Collaborative prediction and ranking with non-random missing data. RecSys 2009: 5-12 - 2008
- [c35]Xuming He, Richard S. Zemel:
Latent topic random fields: Learning using a taxonomy of labels. CVPR 2008 - [c34]Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis:
Learning stick-figure models using nonparametric Bayesian priors over trees. CVPR 2008 - [c33]David A. Ross, Daniel Tarlow, Richard S. Zemel:
Unsupervised Learning of Skeletons from Motion. ECCV (3) 2008: 560-573 - [c32]Xuming He, Richard S. Zemel:
Learning Hybrid Models for Image Annotation with Partially Labeled Data. NIPS 2008: 625-632 - [c31]Rama Natarajan, Iain Murray, Ladan Shams, Richard S. Zemel:
Characterizing response behavior in multisensory perception with conflicting cues. NIPS 2008: 1153-1160 - [c30]Tanya Schmah, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen C. Strother:
Generative versus discriminative training of RBMs for classification of fMRI images. NIPS 2008: 1409-1416 - [c29]Daniel Tarlow, Richard S. Zemel, Brendan J. Frey:
Flexible Priors for Exemplar-based Clustering. UAI 2008: 537-545 - 2007
- [c28]Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Collaborative Filtering and the Missing at Random Assumption. UAI 2007: 267-275 - 2006
- [c27]Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel:
Automated Detection of Unusual Events on Stairs. CRV 2006: 5 - [c26]Xuming He, Richard S. Zemel, Debajyoti Ray:
Learning and Incorporating Top-Down Cues in Image Segmentation. ECCV (1) 2006: 338-351 - [c25]David A. Ross, Simon Osindero, Richard S. Zemel:
Combining discriminative features to infer complex trajectories. ICML 2006: 761-768 - 2005
- [c24]Benjamin M. Marlin, Sam T. Roweis, Richard S. Zemel:
Unsupervised Learning with Non-Ignorable Missing Data. AISTATS 2005 - 2004
- [c23]Xuming He, Richard S. Zemel, Miguel Á. Carreira-Perpiñán:
Multiscale Conditional Random Fields for Image Labeling. CVPR (2) 2004: 695-702 - [c22]Benjamin M. Marlin, Richard S. Zemel:
The multiple multiplicative factor model for collaborative filtering. ICML 2004 - [c21]Miguel Á. Carreira-Perpiñán, Richard S. Zemel:
Proximity Graphs for Clustering and Manifold Learning. NIPS 2004: 225-232 - [c20]Richard S. Zemel, Quentin J. M. Huys, Rama Natarajan, Peter Dayan:
Probabilistic Computation in Spiking Populations. NIPS 2004: 1609-1616 - 2003
- [c19]Richard S. Zemel, Craig Boutilier:
An Active Approach to Collaborative Filtering. AISTATS 2003 - [c18]Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin:
Active Collaborative Filtering. UAI 2003: 98-106 - [c17]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. UAI 2003: 575-582 - 2002
- [c16]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Self Supervised Boosting. NIPS 2002: 665-672 - [c15]David A. Ross, Richard S. Zemel:
Multiple Cause Vector Quantization. NIPS 2002: 1017-1024 - 2000
- [c14]Richard S. Zemel, Toniann Pitassi:
A Gradient-Based Boosting Algorithm for Regression Problems. NIPS 2000: 696-702 - 1999
- [c13]Richard S. Zemel, Michael Mozer:
A Generative Model for Attractor Dynamics. NIPS 1999: 80-88 - [c12]Zhiyong Yang, Richard S. Zemel:
Managing Uncertainty in Cue Combination. NIPS 1999: 869-878 - 1998
- [c11]Richard S. Zemel, Peter Dayan:
Distributional Population Codes and Multiple Motion Models. NIPS 1998: 174-182 - 1997
- [c10]Richard S. Zemel, Peter Dayan:
Combining Probabilistic Population Codes. IJCAI 1997: 1114-1119 - 1996
- [c9]Richard S. Zemel, Peter Dayan, Alexandre Pouget:
Probabilistic Interpretation of Population Codes. NIPS 1996: 676-684 - [c8]Michael S. Gray, Alexandre Pouget, Richard S. Zemel, Steven J. Nowlan, Terrence J. Sejnowski:
Selective Integration: A Model for Disparity Estimation. NIPS 1996: 866-872 - 1994
- [c7]Richard S. Zemel, Terrence J. Sejnowski:
Grouping Components of Three-Dimensional Moving Objects in Area MST of Visual Cortex. NIPS 1994: 165-172 - 1993
- [c6]Geoffrey E. Hinton, Richard S. Zemel:
Autoencoders, Minimum Description Length and Helmholtz Free Energy. NIPS 1993: 3-10 - [c5]Richard S. Zemel, Geoffrey E. Hinton:
Developing Population Codes by Minimizing Description Length. NIPS 1993: 11-18 - 1992
- [c4]Richard S. Zemel, Christopher K. I. Williams, Michael Mozer:
Directional-Unit Boltzmann Machines. NIPS 1992: 172-179 - 1991
- [c3]Michael Mozer, Richard S. Zemel, Marlene Behrmann:
Learning to Segment Images Using Dynamic Feature Binding. NIPS 1991: 436-443 - 1990
- [c2]Richard S. Zemel, Geoffrey E. Hinton:
Discovering Viewpoint-Invariant Relationships That Characterize Objects. NIPS 1990: 299-305 - 1989
- [c1]Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton:
TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations. NIPS 1989: 266-273
Parts in Books or Collections
- 2003
- [p1]Richard S. Zemel:
Cortical Belief Networks. Computational Models for Neuroscience 2003: 267-287
Editorship
- 2021
- [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] - 2011
- [e2]John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger:
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. 2011 [contents] - 2010
- [e1]John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta:
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. Curran Associates, Inc. 2010 [contents]
Informal and Other Publications
- 2024
- [i94]Elliot Creager, Richard S. Zemel:
Online Algorithmic Recourse by Collective Action. CoRR abs/2401.00055 (2024) - [i93]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) - 2023
- [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
- [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
- [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
- [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) - 2019
- [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
- [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]Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel:
Reviving and Improving Recurrent Back-Propagation. CoRR abs/1803.06396 (2018) - [i41]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. CoRR abs/1803.07710 (2018) - [i40]James Lucas, Richard S. Zemel, Roger B. Grosse:
Aggregated Momentum: Stability Through Passive Damping. CoRR abs/1804.00325 (2018) - [i39]Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger B. Grosse, Richard S. Zemel:
Adversarial Distillation of Bayesian Neural Network Posteriors. CoRR abs/1806.10317 (2018) - [i38]Amir Rosenfeld, Richard S. Zemel, John K. Tsotsos:
The Elephant in the Room. CoRR abs/1808.03305 (2018) - [i37]David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel:
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. CoRR abs/1809.02519 (2018) - [i36]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. CoRR abs/1809.02840 (2018) - [i35]Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel:
Understanding the Origins of Bias in Word Embeddings. CoRR abs/1810.03611 (2018) - [i34]Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. CoRR abs/1810.07218 (2018) - [i33]Jörn-Henrik Jacobsen, Jens Behrmann, Richard S. Zemel, Matthias Bethge:
Excessive Invariance Causes Adversarial Vulnerability. CoRR abs/1811.00401 (2018) - [i32]Jack Klys, Jake Snell, Richard S. Zemel:
Learning Latent Subspaces in Variational Autoencoders. CoRR abs/1812.06190 (2018) - 2017
- [i31]Wenjie Luo, Yujia Li, Raquel Urtasun, Richard S. Zemel:
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. CoRR abs/1701.04128 (2017) - [i30]Jake Snell, Kevin Swersky, Richard S. Zemel:
Prototypical Networks for Few-shot Learning. CoRR abs/1703.05175 (2017) - [i29]Christos Louizos, Uri Shalit, Joris M. Mooij, David A. Sontag, Richard S. Zemel, Max Welling:
Causal Effect Inference with Deep Latent-Variable Models. CoRR abs/1705.08821 (2017) - [i28]Yujia Li, Alexander G. Schwing, Kuan-Chieh Wang, Richard S. Zemel:
Dualing GANs. CoRR abs/1706.06216 (2017) - [i27]Eleni Triantafillou, Richard S. Zemel, Raquel Urtasun:
Few-Shot Learning Through an Information Retrieval Lens. CoRR abs/1707.02610 (2017) - [i26]David Madras, Toniann Pitassi, Richard S. Zemel:
Predict Responsibly: Increasing Fairness by Learning To Defer. CoRR abs/1711.06664 (2017) - 2016
- [i25]Mengye Ren, Richard S. Zemel:
End-to-End Instance Segmentation and Counting with Recurrent Attention. CoRR abs/1605.09410 (2016) - [i24]Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel:
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. CoRR abs/1611.04520 (2016) - 2015
- [i23]Yujia Li, Kevin Swersky, Richard S. Zemel:
Generative Moment Matching Networks. CoRR abs/1502.02761 (2015) - [i22]Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio:
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. CoRR abs/1502.03044 (2015) - [i21]Mengye Ren, Ryan Kiros, Richard S. Zemel:
Image Question Answering: A Visual Semantic Embedding Model and a New Dataset. CoRR abs/1505.02074 (2015) - [i20]Yukun Zhu, Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler:
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books. CoRR abs/1506.06724 (2015) - [i19]Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler:
Skip-Thought Vectors. CoRR abs/1506.06726 (2015) - [i18]Karl Ridgeway, Jake Snell, Brett Roads, Richard S. Zemel, Michael C. Mozer:
Learning to generate images with perceptual similarity metrics. CoRR abs/1511.06409 (2015) - [i17]Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Direct Loss Minimization for Training Deep Neural Nets. CoRR abs/1511.06411 (2015) - 2014
- [i16]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR abs/1402.0929 (2014) - [i15]Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov:
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations. CoRR abs/1406.2710 (2014) - [i14]Yujia Li, Richard S. Zemel:
Mean-Field Networks. CoRR abs/1410.5884 (2014) - [i13]Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel:
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. CoRR abs/1411.2539 (2014) - [i12]Yujia Li, Kevin Swersky, Richard S. Zemel:
Learning unbiased features. CoRR abs/1412.5244 (2014) - 2012
- [i11]Laurent Charlin, Richard S. Zemel, Craig Boutilier:
A Framework for Optimizing Paper Matching. CoRR abs/1202.3706 (2012) - [i10]Daniel Tarlow, Richard S. Zemel, Brendan J. Frey:
Flexible Priors for Exemplar-based Clustering. CoRR abs/1206.3294 (2012) - [i9]Laurent Charlin, Richard S. Zemel, Craig Boutilier:
Active Learning for Matching Problems. CoRR abs/1206.4647 (2012) - [i8]Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Collaborative Filtering and the Missing at Random Assumption. CoRR abs/1206.5267 (2012) - [i7]Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey:
Fast Exact Inference for Recursive Cardinality Models. CoRR abs/1210.4899 (2012) - [i6]Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin:
Active Collaborative Filtering. CoRR abs/1212.2442 (2012) - [i5]Max Welling, Richard S. Zemel, Geoffrey E. Hinton:
Efficient Parametric Projection Pursuit Density Estimation. CoRR abs/1212.2513 (2012) - 2011
- [i4]Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard S. Zemel:
Fairness Through Awareness. CoRR abs/1104.3913 (2011) - [i3]Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey:
Interpreting Graph Cuts as a Max-Product Algorithm. CoRR abs/1105.1178 (2011) - [i2]Ryan Prescott Adams, Richard S. Zemel:
Ranking via Sinkhorn Propagation. CoRR abs/1106.1925 (2011) - [i1]Maksims Volkovs, Hugo Larochelle, Richard S. Zemel:
Loss-sensitive Training of Probabilistic Conditional Random Fields. CoRR abs/1107.1805 (2011)
Coauthor Index
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