610 citations · 4.5k across the 98 of their papers we have counts for
103 papers
Improving score-based sampling via affine post-processing
Curtis McDonald, Kevin Rojas, Molei Tao +1
Sampling from a probability distribution with access only to its log-density is a foundational problem in statistics, machine learning, and the physical sciences. This problem is s…
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…
Trained Transformers Learn Linear Models In-Context
Ruiqi Zhang, Spencer Frei, Peter L. Bartlett
Attention-based neural networks such as transformers have demonstrated a remarkable ability to exhibit in-context learning (ICL): Given a short prompt sequence of tokens from an un…
Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions
Peter L. Bartlett, Philip M. Long
We present a new general-purpose algorithm for learning classes of -valued functions in a generalization of the prediction model, and prove a general upper bound on the expe…