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Found 7 papers, 0 papers with code
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits.
Communication-Efficient Collaborative Best Arm Identification
We investigate top-$m$ arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function.
Parallel Best Arm Identification in Heterogeneous Environments
In this paper, we study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model, where multiple agents interact with possibly different environments and they want to learn in parallel an objective function in the aggregated environment.
Batched Thompson Sampling for Multi-Armed Bandits
We study Thompson Sampling algorithms for stochastic multi-armed bandits in the batched setting, in which we want to minimize the regret over a sequence of arm pulls using a small number of policy changes (or, batches).
Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit
Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade.
Batched Coarse Ranking in Multi-Armed Bandits
We study the problem of coarse ranking in the multi-armed bandits (MAB) setting, where we have a set of arms each of which is associated with an unknown distribution.
Collaborative Top Distribution Identifications with Limited Interaction
We give optimal time-round tradeoffs, as well as demonstrate complexity separations between top-$1$ arm identification and top-$m$ arm identifications for general $m$ and between fixed-time and fixed-confidence variants.
