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Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
This methodology enables us to formulate an instance-dependent frequentist regret bound, which incorporates the geometric information, for a broad class of base algorithms, including Greedy, OFUL, and Thompson sampling.
Dynamic Regret Minimization for Control of Non-stationary Linear Dynamical Systems
Under the assumption that a sequence of stabilizing, but potentially sub-optimal controllers is available for all $t$, we present an algorithm that achieves the optimal dynamic regret of $\tilde{\mathcal{O}}\left(V_T^{2/5}T^{3/5}\right)$.
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
We consider the bipartite graph and formalize its representation learning problem as a statistical estimation problem of parameters in a semiparametric exponential family distribution.
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator
Multi-agent reinforcement learning has been successfully applied to a number of challenging problems.
Multi-agent Reinforcement Learning
reinforcement-learning
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