17 citations · 27 across the 9 of their papers we have counts for
8 papers · 1 filter
Swap Regret Minimization Through Response-Based Approachability
Ioannis Anagnostides, Gabriele Farina, Maxwell Fishelson +2
We consider the problem of minimizing different notions of swap regret in online optimization. These forms of regret are tightly connected to correlated equilibrium concepts in gam…
High-Dimensional Calibration from Swap Regret
Maxwell Fishelson, Noah Golowich, Mehryar Mohri +1
We study online calibration of multi-dimensional forecasts over an arbitrary convex set relative to an arbitrary norm . We connect this to externa…
Full Swap Regret and Discretized Calibration
Maxwell Fishelson, Robert Kleinberg, Princewill Okoroafor +3
We study the problem of minimizing swap regret in structured normal-form games. Players have a very large (potentially infinite) number of pure actions, but each action has an embe…
Breaking the Barrier for Sequential Calibration
Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson +3
A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on the subset of timesteps where tha…
From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces
Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson +1
We provide a novel reduction from swap-regret minimization to external-regret minimization, which improves upon the classical reductions of Blum-Mansour [BM07] and Stolz-Lugosi [SL…
Online Learning and Solving Infinite Games with an ERM Oracle
Angelos Assos, Idan Attias, Yuval Dagan +2
While ERM suffices to attain near-optimal generalization error in the stochastic learning setting, this is not known to be the case in the online learning setting, where algorithms…
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games
Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina +3
Recently, Daskalakis, Fishelson, and Golowich (DFG) (NeurIPS`21) showed that if all agents in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights U…
Near-Optimal No-Regret Learning in General Games
Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich
We show that Optimistic Hedge -- a common variant of multiplicative-weights-updates with recency bias -- attains regret in multi-player general-sum games. In p…