174 citations · 700 across the 32 of their papers we have counts for
31 papers · 1 filter
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…
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
Spencer Frei, Gal Vardi, Peter L. Bartlett +2
The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit…
Adversarial Examples in Multi-Layer Random ReLU Networks
Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri
We consider the phenomenon of adversarial examples in ReLU networks with independent gaussian parameters. For networks of constant depth and with a large range of widths (for insta…
Preference learning along multiple criteria: A game-theoretic perspective
Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett +2
The literature on ranking from ordinal data is vast, and there are several ways to aggregate overall preferences from pairwise comparisons between objects. In particular, it is wel…
Agnostic learning with unknown utilities
Kush Bhatia, Peter L. Bartlett, Anca D. Dragan +1
Traditional learning approaches for classification implicitly assume that each mistake has the same cost. In many real-world problems though, the utility of a decision depends on t…