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Symmetric Linear Bandits with Hidden Symmetry
Our algorithm achieves a regret bound of $ O(d_0^{1/3} T^{2/3} \log(d))$, where $d$ is the ambient dimension which is potentially very large, and $d_0$ is the dimension of the true low-dimensional subspace such that $d_0 \ll d$.
Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
Within the class of strictly convex games, we present an algorithm named \texttt{Common-Points-Picking} that returns a point in the expected core given a polynomial number of samples, with high probability.
Invariant Lipschitz Bandits: A Side Observation Approach
Using the side-observation approach, we prove an improved regret upper bound, which depends on the cardinality of the group, given that the group is finite.
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