stat.ML · 2026-06-10 · No. 19

Machine Learning, 2026-06-10.

4 new papers in stat.ML. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Itô maps for any-step SDEs

    Zhengkai Pan, Peter Potaptchik, Wenxi Yao, Michael S. Albergo, Jakiw Pidstrigach

    stat.ML · cs.LG

    Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning from ordinary differential equations, leaving open how to define an exact distillation procedure for stochastic dynamics. We introduce the Itô map, an any-step stochastic flow map that takes an intermediate state and Brownian path and predicts future states in a single pass. The Itô map...

    arxiv.org/abs/2606.11156 · PDF

  2. 02

    Generalized Conformal Predictive Systems Under Distributional Shifts

    Jef Jonkers, Johanna Ziegel

    stat.ML · cs.LG

    Conformal predictive systems (CPS) output calibrated bands of CDFs under exchangeability. We extend generalized CPS to non-exchangeable settings by encoding distributional shifts through observation-specific permutation weights. This yields shift-aware predictive systems that remain valid whenever the test point is, conditionally on the unordered sample, a weighted draw from the observed atoms. Since such weights are typically estimated, we...

    arxiv.org/abs/2606.11044 · PDF

  3. 03

    Range Penalization: Theoretical Insights with Applications in Federated Learning

    Yiyuan She, Zhaojun Hu, Yifan Sun

    stat.ML · cs.LG · math.ST · stat.ME

    This paper introduces range regularization for federated learning with linear systematic components to enhance statistical accuracy and induce cross-client regularity conducive to quantization, coding, and resource efficiency. Our approach identifies features with shared weights across different clients and adaptively clusters the weights of personalized features at extreme values, a process we refer to as polar clustering. Theoretical...

    arxiv.org/abs/2606.10916 · PDF

  4. 04

    Human-AI Teaming Through the Lens of Calibration

    Eric Nalisnick, Chi Zhang, Sophia Qian, Yixin Wang

    stat.ML · cs.AI · cs.LG

    We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect to some partitioning of the feature space -- and expose how the calibration assumptions propagate into the teaming framework. In particular, we consider frameworks that either (i) combine human and model predictions or (ii) delegate prediction responsibility to...

    arxiv.org/abs/2606.10906 · PDF

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