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Richard Zemel

University of Toronto

Online Unsupervised Learning of Visual Representations and Categories


Sep 13, 2021
Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel

* 29 pages 

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Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data


Jul 19, 2021
Jacob Kelly, Richard Zemel, Will Grathwohl


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NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation


Jul 04, 2021
Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao

* UAI2021, code at https://github.com/ZENGXH/NPDRAW 

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NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation


Jun 25, 2021
Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao

* UAI2021, code at https://github.com/ZENGXH/NPDRAW 

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Learning a Universal Template for Few-shot Dataset Generalization


May 14, 2021
Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin


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Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes


Apr 23, 2021
James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard Zemel, Roger Grosse

* 15 pages in main paper, 4 pages of references, 24 pages in appendix. 29 figures in total 

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A Computational Framework for Slang Generation


Feb 03, 2021
Zhewei Sun, Richard Zemel, Yang Xu

* Accepted for publication in TACL 

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A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks


Dec 14, 2020
Renjie Liao, Raquel Urtasun, Richard Zemel


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Flexible Few-Shot Learning with Contextual Similarity


Dec 10, 2020
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel

* Technical report, 29 pages 

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Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification


Dec 02, 2020
Robert Adragna, Elliot Creager, David Madras, Richard Zemel

* 12 pages, 5 figures. Appears in the NeurIPS 2020 Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability 

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Exchanging Lessons Between Algorithmic Fairness and Domain Generalization


Oct 14, 2020
Elliot Creager, J枚rn-Henrik Jacobsen, Richard Zemel


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Theoretical bounds on estimation error for meta-learning


Oct 14, 2020
James Lucas, Mengye Ren, Irene Kameni, Toniann Pitassi, Richard Zemel

* 12 pages in main paper,22 pages in appendix,4 figures total 

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SketchEmbedNet: Learning Novel Concepts by Imitating Drawings


Aug 27, 2020
Alexander Wang, Mengye Ren, Richard Zemel


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Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach


Aug 18, 2020
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier


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Bayesian Few-Shot Classification with One-vs-Each P贸lya-Gamma Augmented Gaussian Processes


Jul 20, 2020
Jake Snell, Richard Zemel

* Submitted to NeurIPS 2020. 20 pages, 5 figures 

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Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data


Jun 18, 2020
Sindy L枚we, David Madras, Richard Zemel, Max Welling


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Shortcut Learning in Deep Neural Networks


May 20, 2020
Robert Geirhos, J枚rn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann

* perspective article 

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Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling


Feb 14, 2020
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel


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A Divergence Minimization Perspective on Imitation Learning Methods


Nov 06, 2019
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu

* Published at Conference on Robot Learning (CoRL) 2019. For datasets and reproducing results please refer to https://github.com/KamyarGh/rl_swiss/blob/master/reproducing/fmax_paper.md 

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Causal Modeling for Fairness in Dynamical Systems


Sep 18, 2019
Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel


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Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models


Jun 22, 2019
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang

* Authors are listed in alphabetical order 

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Flexibly Fair Representation Learning by Disentanglement


Jun 06, 2019
Elliot Creager, David Madras, J枚rn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

* Proceedings of the International Conference on Machine Learning (ICML), 2019 

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Conditional Generative Models are not Robust


Jun 04, 2019
Ethan Fetaya, J枚rn-Henrik Jacobsen, Richard Zemel


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High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks


Mar 26, 2019
Amir Rosenfeld, Richard Zemel, John K. Tsotsos


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Learning Latent Subspaces in Variational Autoencoders


Dec 14, 2018
Jack Klys, Jake Snell, Richard Zemel

* Published as a conference paper at NeurIPS 2018. 15 pages 

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