
What Do Neural Networks Learn When Trained With Random Labels?
We study deep neural networks (DNNs) trained on natural image data with ...
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Proper Learning, Helly Number, and an Optimal SVM Bound
The classical PAC sample complexity bounds are stated for any Empirical ...
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Predicting Neural Network Accuracy from Weights
We study the prediction of the accuracy of a neural network given only i...
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Iterated Jackknives and TwoSided Variance Inequalities
We consider the variance of a function of n independent random variables...
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Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Stateoftheart machine learning methods exhibit limited compositional ...
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Fast classification rates without standard margin assumptions
We consider the classical problem of learning rates for classes with fin...
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Sharper bounds for uniformly stable algorithms
The generalization bounds for stable algorithms is a classical question ...
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Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelera...
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When can unlabeled data improve the learning rate?
In semisupervised classification, one is given access both to labeled a...
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Practical and Consistent Estimation of fDivergences
The estimation of an fdivergence between two probability distributions ...
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Evaluating Generative Models Using Divergence Frontiers
Despite the tremendous progress in the estimation of generative models, ...
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The Optimal Approximation Factor in Density Estimation
Consider the following problem: given two arbitrary densities q_1,q_2 an...
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Passing Tests without Memorizing: Two Models for Fooling Discriminators
We introduce two mathematical frameworks for foolability in the context ...
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Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest...
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Gradient Descent Quantizes ReLU Network Features
Deep neural networks are often trained in the overparametrized regime (...
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Are GANs Created Equal? A LargeScale Study
Generative adversarial networks (GAN) are a powerful subclass of generat...
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Wasserstein AutoEncoders
We propose the Wasserstein AutoEncoder (WAE)a new algorithm for buil...
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Approximation and Convergence Properties of Generative Adversarial Learning
Generative adversarial networks (GAN) approximate a target data distribu...
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Better Text Understanding Through ImageToText Transfer
Generic text embeddings are successfully used in a variety of tasks. How...
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From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transp...
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AdaGAN: Boosting Generative Models
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an e...
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Olivier Bousquet
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