research
∙
09/19/2022
Deep Linear Networks can Benignly Overfit when Shallow Ones Do
We bound the excess risk of interpolating deep linear networks trained u...
share
research
∙
05/26/2022
Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification
While a broad range of techniques have been proposed to tackle distribut...
research
∙
02/15/2022
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
In this work, we provide a characterization of the feature-learning proc...
research
∙
02/11/2022
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Benign overfitting, the phenomenon where interpolating models generalize...
research
∙
12/24/2021
Is Importance Weighting Incompatible with Interpolating Classifiers?
Importance weighting is a classic technique to handle distribution shift...
research
∙
10/06/2021
Foolish Crowds Support Benign Overfitting
We prove a lower bound on the excess risk of sparse interpolating proced...
research
∙
08/25/2021
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
The recent success of neural network models has shone light on a rather ...
research
∙
05/29/2021
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
We study a theory of reinforcement learning (RL) in which the learner re...
research
∙
02/09/2021
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
We establish conditions under which gradient descent applied to fixed-wi...
research
∙
12/04/2020
When does gradient descent with logistic loss find interpolating two-layer networks?
We study the training of finite-width two-layer smoothed ReLU networks f...
research
∙
04/25/2020
Finite-sample analysis of interpolating linear classifiers in the overparameterized regime
We prove bounds on the population risk of the maximum margin algorithm f...
research
∙
02/01/2020
Oracle lower bounds for stochastic gradient sampling algorithms
We consider the problem of sampling from a strongly log-concave density ...
research
∙
12/02/2019
The intriguing role of module criticality in the generalization of deep networks
We study the phenomenon that some modules of deep neural networks (DNNs)...
research
∙
05/30/2019
Langevin Monte Carlo without Smoothness
Langevin Monte Carlo (LMC) is an iterative algorithm used to generate sa...
research
∙
05/24/2019
OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits
We consider the stochastic linear (multi-armed) contextual bandit proble...
research
∙
05/04/2018
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting
We study the problem of sampling from a distribution where the negative ...
research
∙
02/27/2018
Online learning with kernel losses
We present a generalization of the adversarial linear bandits framework,...
research
∙
02/15/2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
We provide convergence guarantees in Wasserstein distance for a variety ...
research
∙
11/09/2017
Alternating minimization for dictionary learning with random initialization
We present theoretical guarantees for an alternating minimization algori...
research
∙
07/12/2017
