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Richard Zemel

University of Toronto

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 

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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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Excessive Invariance Causes Adversarial Vulnerability

Nov 01, 2018
J枚rn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge

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Neural Guided Constraint Logic Programming for Program Synthesis

Oct 26, 2018
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard Zemel

* To appear in NIPS 2018 

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Learning Adversarially Fair and Transferable Representations

Oct 22, 2018
David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel

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Understanding the Origins of Bias in Word Embeddings

Oct 08, 2018
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel

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