arXiv Statistics ML
Finite-Sample FDR Control for Greedy Aggregation over Networks
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On Large-Scale Multiple Testing Over Networks: A Non-Asymptotic Approach
arXiv:2609.14170v1 Announce Type: cross Abstract: Distributed multiple testing asks $N$ sites to control a global false discovery rate (FDR) under a tight communication budget. The greedy interval-ag...
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Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
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Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces
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Sharp Non-Asymptotic Analysis of the Penalized Challenger in $\beta$-EB-TCI for Bernoulli Bandits
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Optimal Experiments for Partial Causal Effect Identification
The paper tackles selecting a cost‑constrained set of experiments that most effectively tighten bounds on a partially identifiable causal query. It formalizes this as the NP‑hard max‑potency problem, introduces efficient graphical pruning rules to reduce the search space, and demonstrates the approach on synthetic graphs and real NHANES data to estimate the effect of physical activity on diabetes.
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Optimal Top-$k$ Identification from Pairwise Comparisons
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