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This paper develops a statistical theory for offline reinforcement learning from trajectory-level outcome supervision, proposing the OPAC algorithm and characterizing when such supervision enables efficient learning versus when fundamental barriers arise.
This paper develops a framework to grade the capability to infer in data-driven systems under the European AI Act, using credit scoring as a case study to illustrate where inference occurs and where regulatory clarity is needed.
This paper presents a computational framework to test competing maturational theories of syntactic development in children, specifically comparing bottom-up versus inward accounts using statistical grammar induction.
This paper proposes Online Localized Conformal Prediction (OLCP) to address covariate heterogeneity in online learning and time-series settings. It introduces OLCP-Hedge for bandwidth selection and demonstrates valid long-run coverage with narrower prediction sets compared to existing baselines.