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09/04/2023
Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD
When using Stochastic Gradient Descent (SGD) for training machine learni...
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06/14/2023
Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning
The success of SGD in deep learning has been ascribed by prior works to ...
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02/13/2023
SubTuning: Efficient Finetuning for Multi-Task Learning
Finetuning a pretrained model has become a standard approach for trainin...
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03/28/2022
Knowledge Distillation: Bad Models Can Be Good Role Models
Large neural networks trained in the overparameterized regime are able t...
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02/20/2022
Deconstructing Distributions: A Pointwise Framework of Learning
In machine learning, we traditionally evaluate the performance of a sing...
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03/11/2021
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions
It is well established that training deep neural networks gives useful r...
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10/16/2020
For self-supervised learning, Rationality implies generalization, provably
We prove a new upper bound on the generalization gap of classifiers that...
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02/21/2020
Robustness from Simple Classifiers
Despite the vast success of Deep Neural Networks in numerous application...
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12/04/2019
Deep Double Descent: Where Bigger Models and More Data Hurt
We show that a variety of modern deep learning tasks exhibit a "double-d...
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05/28/2019
SGD on Neural Networks Learns Functions of Increasing Complexity
We perform an experimental study of the dynamics of Stochastic Gradient ...
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03/09/2019