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08/31/2022
Fine-Grained Distribution-Dependent Learning Curves
Learning curves plot the expected error of a learning algorithm as a fun...
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05/04/2021
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vi...
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06/18/2020
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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02/26/2020
Predicting Neural Network Accuracy from Weights
We study the prediction of the accuracy of a neural network given only i...
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05/28/2019
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled a...
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05/27/2019
Practical and Consistent Estimation of f-Divergences
The estimation of an f-divergence between two probability distributions ...
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01/30/2019
GeNet: Deep Representations for Metagenomics
We introduce GeNet, a method for shotgun metagenomic classification from...
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04/30/2018
Clustering Meets Implicit Generative Models
Clustering is a cornerstone of unsupervised learning which can be though...
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02/11/2018
On the Latent Space of Wasserstein Auto-Encoders
We study the role of latent space dimensionality in Wasserstein auto-enc...
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11/05/2017
Wasserstein Auto-Encoders
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for buil...
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10/04/2017
Differentially Private Database Release via Kernel Mean Embeddings
We lay theoretical foundations for new database release mechanisms that ...
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06/30/2017
Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children fro...
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05/22/2017
From optimal transport to generative modeling: the VEGAN cookbook
We study unsupervised generative modeling in terms of the optimal transp...
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01/09/2017
AdaGAN: Boosting Generative Models
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an e...
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10/19/2016
Consistent Kernel Mean Estimation for Functions of Random Variables
We provide a theoretical foundation for non-parametric estimation of fun...
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02/09/2016
Minimax Lower Bounds for Realizable Transductive Classification
Transductive learning considers a training set of m labeled samples and ...
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05/12/2015
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning
Transductive learning considers situations when a learner observes m lab...
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02/09/2015
Towards a Learning Theory of Cause-Effect Inference
We pose causal inference as the problem of learning to classify probabil...
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11/26/2014
