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12/03/2022
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
We approach the problem of improving robustness of deep learning algorit...
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03/09/2022
Data-Efficient Structured Pruning via Submodular Optimization
Structured pruning is an effective approach for compressing large pre-tr...
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07/21/2021
Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
It can be argued that finding an interpretable low-dimensional represent...
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03/02/2021
Online Adversarial Attacks
Adversarial attacks expose important vulnerabilities of deep learning mo...
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02/18/2021
SVRG Meets AdaGrad: Painless Variance Reduction
Variance reduction (VR) methods for finite-sum minimization typically re...
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06/11/2020
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)
As adaptive gradient methods are typically used for training over-parame...
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05/18/2020
An Analysis of the Adaptation Speed of Causal Models
We consider the problem of discovering the causal process that generated...
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02/24/2020
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
We propose a stochastic variant of the classical Polyak step-size (Polya...
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05/24/2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Recent works have shown that stochastic gradient descent (SGD) achieves ...
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04/18/2019
Reducing Noise in GAN Training with Variance Reduced Extragradient
Using large mini-batches when training generative adversarial networks (...
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10/19/2018
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
We revisit the bias-variance tradeoff for neural networks in light of mo...
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01/12/2018
A3T: Adversarially Augmented Adversarial Training
Recent research showed that deep neural networks are highly sensitive to...
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06/11/2015
