76 citations · 180 across the 5 of their papers we have counts for
8 papers
Explaining generalization in deep learning: progress and fundamental limits
Vaishnavh Nagarajan
This dissertation studies a fundamental open challenge in deep learning theory: why do deep networks generalize well even while being overparameterized, unregularized and fitting t…
A Learning Theoretic Perspective on Local Explainability
Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb +1
In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditio…
Provably Safe PAC-MDP Exploration Using Analogies
Melrose Roderick, Vaishnavh Nagarajan, J. Zico Kolter
A key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration (needed to attain good performance on the task) with safet…
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Vaishnavh Nagarajan, J. Zico Kolter
The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima i…
Generalization in Deep Networks: The Role of Distance from Initialization
Vaishnavh Nagarajan, J. Zico Kolter
Why does training deep neural networks using stochastic gradient descent (SGD) result in a generalization error that does not worsen with the number of parameters in the network? T…
Revisiting Adversarial Risk
Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan +1
Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy. Specifically, they show that no classifi…