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05/23/2022
Throwing Away Data Improves Worst-Class Error in Imbalanced Classification
Class imbalances pervade classification problems, yet their treatment di...
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10/27/2021
Simple data balancing achieves competitive worst-group-accuracy
We study the problem of learning classifiers that perform well across (k...
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03/03/2021
Out of Distribution Generalization in Machine Learning
Machine learning has achieved tremendous success in a variety of domains...
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06/09/2020
Low Distortion Block-Resampling with Spatially Stochastic Networks
We formalize and attack the problem of generating new images from old on...
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02/14/2020
Never Give Up: Learning Directed Exploration Strategies
We propose a reinforcement learning agent to solve hard exploration game...
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09/29/2019
Symplectic Recurrent Neural Networks
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algo...
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07/05/2019
Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
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12/21/2017
Geometrical Insights for Implicit Generative Modeling
Learning algorithms for implicit generative models can optimize a variet...
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03/31/2017
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, b...
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01/26/2017
Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional G...
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01/17/2017
Towards Principled Methods for Training Generative Adversarial Networks
The goal of this paper is not to introduce a single algorithm or method,...
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06/02/2016
Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which join...
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11/20/2015
Unitary Evolution Recurrent Neural Networks
Recurrent neural networks (RNNs) are notoriously difficult to train. Whe...
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05/30/2015