4 papers
Best of both worlds: Stochastic & adversarial best-arm identification
Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +2
We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial sce…
A result relating convex n-widths to covering numbers with some applications to neural networks
Jonathan Baxter, Peter Bartlett
In general, approximating classes of functions defined over high-dimensional input spaces by linear combinations of a fixed set of basis functions or ``features'' is known to be ha…
Reinforcement Learning in POMDP's via Direct Gradient Ascent
Jonathan Baxter, Peter L. Bartlett
This paper discusses theoretical and experimental aspects of gradient-based approaches to the direct optimization of policy performance in controlled POMDPs. We introduce GPOMDP, a…
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett
Benign overfitting, the phenomenon where interpolating models generalize well in the presence of noisy data, was first observed in neural network models trained with gradient desce…