A Bregman divergence approach yields a unified calibeating framework for general proper losses, delivering U-calibration and logarithmic regret for Tsallis losses with weaker dimension dependence than prior work.
USSR computational mathematics and mathematical physics , volume=
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The paper establishes the first tilde O(epsilon^{-1}) upper bounds and matching lower bounds for forward-KL-regularized offline contextual bandits under single-policy concentrability in both tabular and general function approximation settings.
Semi-discrete Flow Matching produces terminal assignment regions that are topologically simple (open, simply connected, homeomorphic to the ball under assumption) yet geometrically distinct from optimal transport Laguerre cells, as they can be non-convex with curved boundaries.
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Calibeating for general proper losses: A Bregman divergence approach
A Bregman divergence approach yields a unified calibeating framework for general proper losses, delivering U-calibration and logarithmic regret for Tsallis losses with weaker dimension dependence than prior work.
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Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
The paper establishes the first tilde O(epsilon^{-1}) upper bounds and matching lower bounds for forward-KL-regularized offline contextual bandits under single-policy concentrability in both tabular and general function approximation settings.
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Tessellations of Semi-Discrete Flow Matching
Semi-discrete Flow Matching produces terminal assignment regions that are topologically simple (open, simply connected, homeomorphic to the ball under assumption) yet geometrically distinct from optimal transport Laguerre cells, as they can be non-convex with curved boundaries.