2.3k citations · 2.7k across the 38 of their papers we have counts for
6 papers · 1 filter
Is Importance Weighting Incompatible with Interpolating Classifiers?
Ke Alexander Wang, Niladri S. Chatterji, Saminul Haque +1
Importance weighting is a classic technique to handle distribution shifts. However, prior work has presented strong empirical and theoretical evidence demonstrating that importance…
Foolish Crowds Support Benign Overfitting
Niladri S. Chatterji, Philip M. Long
We prove a lower bound on the excess risk of sparse interpolating procedures for linear regression with Gaussian data in the overparameterized regime. We apply this result to obtai…
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
The recent success of neural network models has shone light on a rather surprising statistical phenomenon: statistical models that perfectly fit noisy data can generalize well to u…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett +1
We study a theory of reinforcement learning (RL) in which the learner receives binary feedback only once at the end of an episode. While this is an extreme test case for theory, it…
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations?
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
We establish conditions under which gradient descent applied to fixed-width deep networks drives the logistic loss to zero, and prove bounds on the rate of convergence. Our analysi…