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Bandits with Switching Costs: T^{2/3} Regret

Bandits with Switching Costs: T^{2/3} Regret

11 October 2013
O. Dekel
Jian Ding
Tomer Koren
Yuval Peres
ArXivPDFHTML

Papers citing "Bandits with Switching Costs: T^{2/3} Regret"

16 / 16 papers shown
Title
A Simple and Adaptive Learning Rate for FTRL in Online Learning with
  Minimax Regret of $Θ(T^{2/3})$ and its Application to
  Best-of-Both-Worlds
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of Θ(T2/3)Θ(T^{2/3})Θ(T2/3) and its Application to Best-of-Both-Worlds
Taira Tsuchiya
Shinji Ito
28
0
0
30 May 2024
Multi-armed Bandit Learning on a Graph
Multi-armed Bandit Learning on a Graph
Tianpeng Zhang
Kasper Johansson
Na Li
35
6
0
20 Sep 2022
A Simple and Provably Efficient Algorithm for Asynchronous Federated
  Contextual Linear Bandits
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
Jiafan He
Tianhao Wang
Yifei Min
Quanquan Gu
FedML
45
32
0
07 Jul 2022
Best Arm Identification in Restless Markov Multi-Armed Bandits
Best Arm Identification in Restless Markov Multi-Armed Bandits
P. Karthik
Kota Srinivas Reddy
Vincent Y. F. Tan
30
4
0
29 Mar 2022
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
Chloé Rouyer
Yevgeny Seldin
Nicolò Cesa-Bianchi
AAML
26
24
0
19 Feb 2021
Non-stationary Online Learning with Memory and Non-stochastic Control
Non-stationary Online Learning with Memory and Non-stochastic Control
Peng Zhao
Yu-Hu Yan
Yu Wang
Zhi-Hua Zhou
45
47
0
07 Feb 2021
Provably Efficient Reinforcement Learning with Linear Function
  Approximation Under Adaptivity Constraints
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
Chi Jin
Zhuoran Yang
Zhaoran Wang
OffRL
122
167
0
06 Jan 2021
A Provably Efficient Sample Collection Strategy for Reinforcement
  Learning
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
Jean Tarbouriech
Matteo Pirotta
Michal Valko
A. Lazaric
OffRL
35
16
0
13 Jul 2020
Linear Bandits with Limited Adaptivity and Learning Distributional
  Optimal Design
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Yufei Ruan
Jiaqi Yang
Yuanshuo Zhou
OffRL
113
51
0
04 Jul 2020
Bayesian optimization for modular black-box systems with switching costs
Bayesian optimization for modular black-box systems with switching costs
Chi-Heng Lin
Joseph D. Miano
Eva L. Dyer
13
5
0
04 Jun 2020
Minimax Regret of Switching-Constrained Online Convex Optimization: No
  Phase Transition
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
Lin Chen
Qian-long Yu
Hannah Lawrence
Amin Karbasi
24
20
0
24 Oct 2019
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With
  Collision Information, Sublinear Without
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
Sébastien Bubeck
Yuanzhi Li
Yuval Peres
Mark Sellke
16
45
0
28 Apr 2019
Truth and Regret in Online Scheduling
Truth and Regret in Online Scheduling
Shuchi Chawla
Nikhil R. Devanur
Janardhan Kulkarni
Rad Niazadeh
21
13
0
01 Mar 2017
Bandits with Movement Costs and Adaptive Pricing
Bandits with Movement Costs and Adaptive Pricing
Tomer Koren
Roi Livni
Yishay Mansour
27
20
0
24 Feb 2017
On the Complexity of Bandit Linear Optimization
On the Complexity of Bandit Linear Optimization
Ohad Shamir
45
14
0
11 Aug 2014
Chasing Ghosts: Competing with Stateful Policies
Chasing Ghosts: Competing with Stateful Policies
U. Feige
Tomer Koren
Moshe Tennenholtz
38
9
0
29 Jul 2014
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