Results 71 to 80 of about 4,126,170 (169)

EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits [PDF]

, 2022
In this paper, we propose a novel neural exploration strategy in contextual bandits, EE-Net, distinct from the standard UCB-based and TS-based approaches. Contextual multi-armed bandits have been studied for decades with various applications.
He, Jingrui, Ban, Yikun, Yan, Yuchen, Banerjee, Arindam   +3 more
core  

Long–term information collection with energy harvesting wireless sensors: a multi–armed bandit based approach

, 2011
This paper reports on the development of a multi–agent approach to long-term information collection in networks of energy harvesting wireless sensors.
Jennings, Nick, Tran-Thanh, Long, Rogers, Alex, Long, T-T, Rogers, A, Alex Rogers, Nicholas R. Jennings, Jennings, NR, Long Tran-Thanh   +8 more
core   +1 more source

AB Testing for Process Versions with Contextual Multi-armed Bandit Algorithms

, 2018
Business process improvement ideas can be validated through sequential experiment techniques like AB Testing. Such approaches have the inherent risk of exposing customers to an inferior process version, which is why the inferior version should be discarded as quickly as possible.
Suhrid Satyal, Ingo Weber, Hye-young Paik, Claudio Di Ciccio, Jan Mendling   +4 more
openaire   +5 more sources

Adversarial Attacks on Combinatorial Multi-Armed Bandits [PDF]

We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB.
Tadepalli, Prasad, Balasubramanian, Rishab, Wang, Huazheng, Wu, Qingyun, Li, Jiawei, Zhao, Haoyu   +5 more
core   +1 more source

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  

Introduction to multi-armed bandits

, 2019
Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years.
Slivkins, Aleksandrs
core  

Human behavior in contextual multi-armed bandit problems.

, 2015
In real-life decision environments people learn from their direct experience with alternative courses of action. Yet they can accelerate their learning by using functional knowledge about the features characterizing the alternatives. We designed a novel contextual multi-armed bandit task where decision makers chose repeatedly between multiple ...
Stojic, Hrvoje, Analytis, Pantelis P, Speekenbrink, Maarten   +2 more
openaire   +1 more source

Introduction to Multi-Armed Bandits [PDF]

Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory,
Slivkins, Aleksandrs
core   +1 more source

Learning and decisions in contextual multi-armed bandit tasks.

, 2015
Contextual Multi-Armed Bandit (CMAB) tasks are a novel framework to assess decision making in uncertain environments. In a CMAB task, participants are presented with multiple options (arms) which are characterized by a number of features (context) related to the reward associated with the arms.
Schulz, Erich, Konstantinidis, Emmanouil, Speekenbrink, Maarten   +2 more
openaire   +2 more sources

Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits

CoRR, 2013
We present Exponentiated Gradient LINUCB, an algorithm for con-textual multi-armed bandits. This algorithm uses Exponentiated Gradient to find the optimal exploration of the LINUCB. Within a deliberately designed offline simulation framework we conduct evaluations with real online event log data.
openaire   +3 more sources