Results 81 to 90 of about 3,144 (205)

Risk-Aversion in Multi-armed Bandits

CoRR, 2012
(2012)
Sani, Amir, Lazaric, Alessandro, Munos, Rémi   +2 more
openaire   +5 more sources

Combinatorial Multi-Armed Bandit with General Reward Functions [PDF]

, 2016
Wei Chen, Wei Hu, Fu Li, Jian Li, Yu Liu, Pinyan Lu   +5 more
openalex   +1 more source

Collaborative DNN inference in maritime edge intelligence networks with group neural multi-armed bandits

Intelligent and Converged Networks
Collaborative Deep Neural Networks (DNNs) inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things (IoT) devices in maritime edge intelligence networks.
Yulei Wang, Shishi Liu, Di Bai, Yongqiang Cui   +3 more
doaj   +1 more source

Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits [PDF]

, 2019
Tao Chao, Qin Zhang, Yuan Zhou
openalex   +1 more source

State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards

Algorithms
While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios.
Yuto Tanimoto, Kenji Fukumizu
doaj   +1 more source

A state-of-the-art survey and benchmarking of Adaptive Modulation and Coding for Underwater Acoustic Communications

Applied Ocean Research
Adaptive Modulation and Coding (AMC) is a critical technique for optimising data transmission in the highly variable and challenging domain of Underwater Acoustic Communication (UWAC).
Zachary Cooper-Baldock, Eirini Panteli, Paulo E. Santos   +2 more
doaj   +1 more source

Adaptive and Explainable Test Case Prioritization in Continuous Integration: A Systematic Review of Reinforcement Learning, Learning-to-Rank, and Search-/Combinatorial-Based Methods

IEEE Access
Continuous Integration requires fast fault exposure under strict time budgets. Test case prioritization orders regression tests to expose failures early while respecting budget limits and operational constraints.
Srinivasa Rao Kongarana, Ananda Rao Akepogu, Palagiri Radhika Raju   +2 more
doaj   +1 more source

Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits [PDF]

, 2019
Xi Liu, Ping-Chun Hsieh, Anirban Bhattacharya, P. R. Kumar   +3 more
openalex   +1 more source

GNU Radio Implementation of MALIN: “Multi-Armed bandits Learning for Internet-of-things Networks” [PDF]

, 2019
Lilian Besson, Rémi Bonnefoi, Christophe Moy   +2 more
openalex   +1 more source

Starlet: Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

Digital Communications and Networks
The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things. To guarantee the network's overall security, we present a network defense resource allocation with multi-armed ...
Hui Xia, Ning Huang, Xuecai Feng, Rui Zhang, Chao Liu   +4 more
doaj   +1 more source