A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
Jianqing Fan, Han Liu, Qiang Sun, and Tong Zhang
6 Pith papers cite this work. Polarity classification is still indexing.
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GL-LowPopArt is a Catoni-style two-stage estimator for generalized low-rank trace regression that attains state-of-the-art bounds and nearly instance-wise minimax optimality up to the Hessian condition number.
AgentFairBench is a multi-domain benchmark for demographic disparity in LLM agent actions, with a pilot showing no significant effect for Claude Haiku 4.5 after arity-matched noise correction.
NLRΔ applied to 50 primary measures from five cognitive task families on public test-retest data yields median -0.138 nats with zero cells passing the headline rule across a 24-specification multiverse.
Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.
citing papers explorer
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
GL-LowPopArt is a Catoni-style two-stage estimator for generalized low-rank trace regression that attains state-of-the-art bounds and nearly instance-wise minimax optimality up to the Hessian condition number.
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AgentFairBench: Do LLM Agents Discriminate When They Act?
AgentFairBench is a multi-domain benchmark for demographic disparity in LLM agent actions, with a pilot showing no significant effect for Claude Haiku 4.5 after arity-matched noise correction.
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Information-Theoretic Reliability is Robust to Analytic Choice: A 24-Specification Multiverse on Public Cognitive Test-Retest Data
NLRΔ applied to 50 primary measures from five cognitive task families on public test-retest data yields median -0.138 nats with zero cells passing the headline rule across a 24-specification multiverse.
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Sequential Bayesian Monitoring for Recoverable and Drifting Processes
Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.
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