Results 141 to 150 of about 492,986 (210)

Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making

This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model ...
Chen, Xin, Hou, I-Hong
core  

Transfer Learning for Contextual Multi-armed Bandits [PDF]

Motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on source bandits before the start of the target ...
Li, Hongzhe, Cai, Changxiao, Cai, T. Tony   +2 more
core  

To Ask or Not To Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding. [PDF]

IEEE Int Conf Robot Autom
Banerjee R, Jenamani RK, Vasudev S, Nanavati A, Dimitropoulou K, Dean S, Bhattacharjee T.   +6 more
europepmc   +1 more source

Contextual Bandits with Budgeted Information Reveal. [PDF]

Proc Mach Learn Res
Gan K, Keyvanshokooh E, Liu X, Murphy S.
europepmc   +1 more source

Multi-Armed Bandits in Brain-Computer Interfaces. [PDF]

Front Hum Neurosci, 2022
Heskebeck F, Bergeling C, Bernhardsson B.   +2 more
europepmc   +1 more source

Bandits in Information Retrieval

, 2016
This thesis is a review of bandit algorithms in information retrieval. In information retrieval a result list should include the most relevant documents and the results should also be non-redundant and diverse.
Koivisto, Timo
core  

The epoch-greedy algorithm for contextual multi-armed bandits

, 2008
We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also known as bandits with side information). Epoch-Greedy has the following prop-erties: 1. No knowledge of a time horizon T is necessary. 2.
Tong Zhang, John Langford
core  

Negatively Correlated Bandits [PDF]

We analyze a two-player game of strategic experimentation with two-armed bandits. Each player has to decide in continuous time whether to use a safe arm with a known payoff or a risky arm whose likelihood of delivering payoffs is initially unknown.
Sven Rady, Nicolas Klein
core  

Replicable Bandits for Digital Health Interventions. [PDF]

Stat Sci
Zhang KW, Closser N, Trella AL, Murphy SA.   +3 more
europepmc   +1 more source

VacSIM: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning. [PDF]

Intell Based Med, 2022
Awasthi R, Guliani KK, Khan SA, Vashishtha A, Gill MS, Bhatt A, Nagori A, Gupta A, Kumaraguru P, Sethi T.   +9 more
europepmc   +1 more source