stat.ML · 2026-09-24 · No. 123
Machine Learning, 2026-09-24.
6 new papers in stat.ML. Titles, authors,
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01 — The papers
6 entries-
01
How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?
Chen Yang, Xianyang Zhang, Jun Chen
stat.ML · cs.LG
LLM leaderboard gains can reflect selection among privately evaluated model variants, yet neither the number of variants nor their dependence is public. We ask how many hidden variants a published margin can support while retaining statistical evidence of a provider's advantage over a fixed comparator. For a fixed candidate family under a Gaussian margin model, we derive a sensitivity curve that reports this maximum count as a function of a...
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02
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
Andrea Nava, Ken Rölli, Armin Begic, Fabio Sigrist
stat.ML · cs.LG
Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Most extensions of NPs focus on improving the neural network architecture. We instead develop an extension motivated by the shared hierarchical interpretation of meta-learning and mixed-effects models. Specifically, we introduce Neural Process Boosting (NPBoost), which decomposes structured response...
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03
Improving Ensemble Filters with Flow Matching
Haoyuan Chen, Alexandre Thiéry
stat.ML · cs.LG · math.DS · stat.ME
Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow matching to transport the forecast ensemble from a classical baseline filter to an analysis ensemble. FlowEF uses a localized Gaussian source during...
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04
FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints
Sultan Amed, Tanmay Sen, Sayantan Banerjee
stat.ML · cs.LG · q-fin.ST
Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending, overly conservative offers, or rejection of creditworthy applicants. Cross-institutional data-sharing constraints make this problem especially difficult for smaller lenders with limited training data. We introduce FedIncome, a federated learning framework for income estimation that enables...
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05
Multitask Regression with Pairwise Fusion
Xiaodong Li, Zhentao Li
stat.ML · cs.LG
We study multitask regression when coefficient sharing can differ by predictor. For a given predictor, many tasks may have the same coefficient while a few differ, and the exceptional tasks need not be the same for another predictor. We describe this structure by two quantities: the number of active predictors and the total number of task coefficients that differ from the most common value for their predictor. We estimate the coefficient...
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06
On the Sample Complexity of Active Learning with Membership Queries
Ganghua Wang, Shaddin Dughmi
stat.ML · cs.LG · math.ST
This work revisits a fundamental question in active learning: how powerful is the ability to synthesize arbitrary queries? Compared to pool-based active learning, where the learner only selects queries from a given unlabeled pool, we find that this seemingly mild change in query ability may dramatically alter the difficulty of statistical learning. In particular, some hypothesis classes that are inherently slow to learn in the pool-based...
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