Jun 9, 2023 · Here we study collaborative linear regression and contextual bandits, where each instance's associated parameters are equal to a global parameter plus a sparse ...
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Sep 12, 2024 · Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits ... bandit problem under the high dimensional linear model. We ...
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. Huang, X., Xu, K., Lee, D., Hassani, H., Bastani, H., & Dobriban, E. 2023.
Jul 16, 2024 · linear regression and regret in contextual bandits, when tasks are sparsely heterogeneous. ... bandit without collaboration, when T = Ω(d2), the ...
We then apply MOLAR to develop methods for sparsely heterogeneous multitask contextual bandits, obtaining improved regret guarantees over single-task bandit ...
Sep 19, 2024 · Applying MOLAR to linear contextual bandits, we also improve current regret bounds for individual bandit instances. To complement the upper ...
Then, pooling data from different bandits helps learn a good representation and reduces the statistical burden of learning by reducing the linear bandit problem ...
Missing: Regression | Show results with:Regression
Collaborative Bandits: Collaborative bandits seek to leverage similarities between heterogeneous clients to improve bandit learning. Clustered bandit algorithms.
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. X Huang, K Xu, D Lee, H Hassani, H Bastani, E Dobriban. arXiv preprint arXiv: ...
Mar 3, 2024 · Keywords: Multi-agent, Bandits, Cooperative ... This work considers a multi-agent linear contextual bandit model with heterogeneity among the agents.
Missing: Regression | Show results with:Regression