A Bregman divergence approach yields a general calibeating framework that achieves U-calibration with logarithmic regret for Tsallis losses and a new regret equality for Be The Regularized Leader.
USSR computational mathematics and mathematical physics , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.
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Nonlinear RMM-GKS for Large-Scale Dynamic and Streaming Inverse Problems with Uncertain Forward Operators
NL-RMM-GKS extends majorization-minimization and Krylov subspace recycling to nonlinear inverse problems with uncertain forward operators, offering alternating minimization, variable projection, and streaming variants for dynamic imaging.