Online Bandit Linear Optimization: A Study
This article introduces the concepts around Online Bandit Linear Optimization and explores an efficient setup called SCRiBLe (Self-Concordant Regularization in Bandit Learning) created by Abernethy et. al.\cite{abernethy}. The SCRiBLe setup and algorithm yield a $O(\sqrt{T})$ regret bound and polynomial run time complexity bound on the dimension of the input space. In this article we build up to the bandit linear optimization case and study SCRiBLe.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On the Complexity of Bandit Linear Optimization
We study the attainable regret for online linear optimization problems with bandit feedback, where unlike the full-information setting, the player can only observe its own loss rather than the full loss vector. We show t…
Online Stochastic Linear Optimization under One-bit Feedback
In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is revealed to the learner at each round. This problem has found many applications including on…
Data Poisoning Attacks on Stochastic Bandits
Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical treatment, and many others. Even though potential attacks agains…
Data PoisoningMulti-Armed BanditsRecommendation SystemsOnline and Bandit Algorithms for Nonstationary Stochastic Saddle-Point Optimization
Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majority of the rich literature available fo…
Multi-agent Reinforcement LearningReinforcement LearningFederated Online and Bandit Convex Optimization
We study the problems of distributed online and bandit convex optimization against an adaptive adversary. We aim to minimize the average regret on $M$ machines working in parallel over $T$ rounds with $R$ intermittent co…