Results 61 to 70 of about 3,144 (205)

A multi-armed bandit approach for exploring partially observed networks

Applied Network Science, 2019
Background real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topology, and nodes of the original network are unknown.
Kaushalya Madhawa, Tsuyoshi Murata
doaj   +1 more source

Neighbor Cell List Optimization in Handover Management Using Cascading Bandits Algorithm

IEEE Access, 2020
Frequent handover is a key challenge in 5G Ultra-Dense Networks (UDN). In this paper, we show the significance of configuring Neighbor Cell List (NCL) in handover procedure.
Chao Wang, Jian Yang, Huasen He, Ruida Zhou, Shuangwu Chen, Xiaofeng Jiang   +5 more
doaj   +1 more source

Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies

Network
Sequential decision-making in dynamic and interconnected environments is a cornerstone of numerous applications, ranging from communication networks and finance to distributed blockchain systems and IoT frameworks. The multi-armed bandit (MAB) problem is
Abdalaziz Sawwan, Jie Wu
doaj   +1 more source

On Penalization in Stochastic Multi-Armed Bandits

IEEE Transactions on Information Theory
We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration. Instead of directly maximizing cumulative expected reward, we need to balance between the total reward and fairness level.
Guanhua Fang, Ping Li 0001, Gennady Samorodnitsky   +2 more
openaire   +2 more sources

Multi-armed Bandits with Missing Outcome

CoRR
38 pages, 5 figures, multi-armed bandits, missing ...
Ilia Mahrooghi, Mahshad Moradi, Sina Akbari, Negar Kiyavash   +3 more
openaire   +3 more sources

Scaling Multi-Armed Bandit Algorithms

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
The Multi-Armed Bandit (MAB) is a fundamental model capturing the dilemma between exploration and exploitation in sequential decision making. At every time step, the decision maker selects a set of arms and observes a reward from each of the chosen arms. In this paper, we present a variant of the problem, which we call the Scaling MAB (S-MAB): The goal
Fouché, E., Komiyama, J., Böhm, K.
openaire   +2 more sources

NeIL: Intelligent Replica Selection for Distributed Applications

IEEE Transactions on Machine Learning in Communications and Networking
Distributed applications such as cloud gaming, streaming, etc., are increasingly using edge-to-cloud infrastructure for high availability and performance.
Faraz Ahmed, Lianjie Cao, Ayush Goel, Puneet Sharma   +3 more
doaj   +1 more source

Imprecise Multi-Armed Bandits

CoRR
We introduce a novel multi-armed bandit framework, where each arm is associated with a fixed unknown credal set over the space of outcomes (which can be richer than just the reward). The arm-to-credal-set correspondence comes from a known class of hypotheses. We then define a notion of regret corresponding to the lower prevision defined by these credal
openaire   +2 more sources

Causally Abstracted Multi-armed Bandits

CoRR
8 pages, 3 figures (main article); 20 pages, 10 figures (appendix); 40th Conference on Uncertainty in Artificial Intelligence (UAI)
Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis, Anisoara Calinescu, Michael J. Wooldridge, Theodoros Damoulas   +6 more
openaire   +5 more sources

Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking [PDF]

, 2023
Iñigo Urteaga, Moulay Zaidane Draidia, Tomer Lancewicki, Shahram Khadivi   +3 more
openalex   +1 more source