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Found 7 papers, 3 papers with code
Efficient Algorithms for Extreme Bandits
In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward.
A/B/n Testing with Control in the Presence of Subpopulations
At every time step, a subpopulation is sampled and an arm is chosen: the resulting observation is an independent draw from the arm conditioned on the subpopulation.
On Limited-Memory Subsampling Strategies for Bandits
There has been a recent surge of interest in nonparametric bandit algorithms based on subsampling.
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity.
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to address them.
Algorithms for Non-Stationary Generalized Linear Bandits
The statistical framework of Generalized Linear Models (GLM) can be applied to sequential problems involving categorical or ordinal rewards associated, for instance, with clicks, likes or ratings.
Weighted Linear Bandits for Non-Stationary Environments
To address this problem, we propose D-LinUCB, a novel optimistic algorithm based on discounted linear regression, where exponential weights are used to smoothly forget the past.
