Reading
Papers
Multi-player
Sample Application paper:
2019-KDD-Scaling Multi-armed Bandits Algorithms
2019-INFOCOM-Distributed Learning and Optimal Assignment in Multiplayer Heterogeneous Networks
2019-INFOCOM- Combinatorial Sleeping Bandits with Fairness Constraints
2020-Thesis-Multi-players Bandit Algorithms for Internet of Things Networks
2020-ICMLWorshop-Stochastic Multi-Player Bandit Learning from Player-dependent FeedbackSample algorithmic paper:
2018-NIPS-Distributed Multi-Player Bandits - a Game of Thrones Approach
2016-ICML-Multi-Player Bandits – a Musical Chairs Approach
2020-AISTAT-A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
2012_TON_Multi-Armed Bandits With Linear Rewards and Individual Observations
2014-TIT-Decentralized Learning for Multi-player Multi-armed BanditsFoundations:
Lec_MCMC for Distributed Opt2020-Arxiv-Bandit Learning in Decentralized Matching Markets