9 citations · 32 across the 12 of their papers we have counts for
14 papers
Mirror Polyak and a Primal-Dual Lifting
Frederik Kunstner, Ryan D'Orazio, Victor S. Portella +1
First-order methods typically require a specific step-size that depends on the regularity conditions of the objective function, such as the smoothness, Lipschitz continuity, or str…
New Perspectives on the Polyak Stepsize: Surrogate Functions and Negative Results
Francesco Orabona, Ryan D'Orazio
The Polyak stepsize has been proven to be a fundamental stepsize in convex optimization, giving near optimal gradient descent rates across a wide range of assumptions. The universa…
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio, Danilo Vucetic, Zichu Liu +3
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minimizing projected Bellman error and min-ma…
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
Samuel Sokota, Ryan D'Orazio, Chun Kai Ling +3
In their seminal work, Nayyar et al. (2013) showed that imperfect information can be abstracted away from common-payoff games by having players publicly announce their policies as…
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Samuel Sokota, Ryan D'Orazio, J. Zico Kolter +5
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm. Our contribution is dem…
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Dustin Morrill, Ryan D'Orazio, Marc Lanctot +3
Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further…