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Found 3 papers, 1 papers with code
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
Motivated by data privacy concerns in these applications, we study the joint differentially private high dimensional sparse linear bandits, where both rewards and contexts are considered as private data.
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
We consider the stochastic linear contextual bandit problem with high-dimensional features.
Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields
Analysis of heterogeneous patterns in complex spatio-temporal data finds usage across various domains in applied science and engineering, including training autonomous vehicles to navigate in complex traffic scenarios.
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