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This paper studies the sample complexity of multicalibration for a sequence of properties that are sequentially identifiable, establishing matching upper and lower bounds up to logarithmic factors.
This paper studies swap-agnostic learning of proper losses, showing that prediction-level comparisons can be controlled jointly via second-order multicalibration, achieving tight rates for finite hypothesis classes and families of losses.
Singular Learning Theory (SLT) uses algebraic geometry to explain why neural networks generalize well despite their degeneracies, introducing the real log canonical threshold (RLCT) as a measure of model complexity.
This paper introduces a theoretical framework for quantifying deployment risk when training and deployment distributions differ due to latent regime dynamics modeled as a Markov-switching process, providing exact decomposition and finite-sample bounds.
FormalSLT is a Lean 4 library that formally proves finite-sample statistical learning theory results (ERM, VC bounds, Rademacher bounds, PAC-Bayes, etc.) with explicit assumptions and zero sorry statements, providing a machine-checked foundation for ML theory.