regret-minimization

This paper extends CONES to time-varying loss functions, showing bounds for regret and movement cost using projected proximal algorithms in convex optimization.

This paper introduces an online variant of assistance games and provides the first provably efficient learning algorithms for both the human and assistant agents, achieving near-optimal regret bounds.

This paper studies regret minimization in Markovian bandits with non-observable states and constrained decision epochs, introducing a generalization called self-degrading Markovian bandits. The authors propose the UCB-NOM algorithm that achieves nearly logarithmic regret and provide bounds that do not depend on the number of states.

This paper introduces Repeated Policy Regret (RP-Regret), a game-theoretic metric for regret minimization in repeated games with adaptive opponents, and proposes three algorithms to minimize it, showing that doing so can lead to cooperative equilibria like in Stag-Hunt.

Jeff Bezos recounts how he used a regret-minimization framework to decide to quit his job at D.E. Shaw and start Amazon, prioritizing avoiding future regret over fear of failure.