Machine Learning
Improved Regret Bounds for Contextual Bandits with Latent State Dynamics
This paper introduces a novel approach to contextual bandits where contexts and rewards are governed by a hidden Markov chain. Unlike previous methods, this work directly models dependencies on hidden states and achieves stronger, high-probability regret bounds. The key innovation lies in a fully ad…
Modeling Nonreciprocal Pairwise Comparisons for Improved Decision Analysis
This paper introduces an additive model for analyzing pairwise comparisons where nonreciprocity is not seen as a defect but as a combination of genuine scale variation and random perturbations. It allows for the estimation of noise levels, assessment of scale variation, and assignment of probabiliti…
Generative Path-Law Jump-Diffusion for Discontinuous Stochastic Trajectories
This paper introduces the Anticipatory Neural Jump-Diffusion (ANJD) flow, a novel generative framework for synthesizing forward-looking, càdlàg stochastic trajectories. The methodology addresses challenges in modeling structural breaks and non-autonomous dynamics by framing path synthesis as a seque…
Optimal Active Learning for Linear System Identification
This paper introduces an active learning algorithm for linear system identification that utilizes optimal centered noise excitation. The method, based on ordinary least squares and semidefinite programming, achieves minimal sample complexity. The authors establish tight lower and upper bounds for sa…