stat.ML · 2026-08-05 · No. 75

Machine Learning, 2026-08-05.

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

01 — The papers

5 entries
  1. 01

    Information-Geometric Forward Policy Training in GFlowNets

    Yordan Raykov, Rodrigo Veiga

    stat.ML · cs.LG

    Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortised inference over discrete and mixed discrete-continuous objects, requiring only an unnormalised target density specified through a reward. In this work, we formulate forward-policy training in GFlowNets through the information geometry of the induced trajectory sampler. Treating the forward policy as an induced trajectory sampler, we show that its intrinsic...

    arxiv.org/abs/2608.03967 · PDF

  2. 02

    Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling

    Hanqin Cai, Longxiu Huang, Jing Qin, Chengyue Wu

    stat.ML · cs.IT · cs.LG · math.NA

    Tensor cross-concentrated sampling (t-CCS) bridges entrywise sampling and t-CUR slice-wise sampling by observing entries only within selected horizontal and lateral slices. Existing t-CCS completion methods, however, assume that the observations are free of gross corruption. In this work, we study robust recovery of a third-order low-tubal-rank tensor from partial t-CCS observations contaminated by sparse, arbitrarily large outliers. We...

    arxiv.org/abs/2608.03928 · PDF

  3. 03

    Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model

    Yufei Wu, Shanqing Gao, Andreas Voss, Francis Tuerlinckx

    stat.ML · cs.LG · stat.AP · stat.ME

    The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers continue to seek inference approaches that are both fast and flexible across diverse study designs. Amortized Bayesian inference (ABI) can provide nearly instantaneous inference for complex stochastic models like the DDM, but neural networks trained for one study design cannot generalize to others. In...

    arxiv.org/abs/2608.03566 · PDF

  4. 04

    Should the Boundary Term Be Learned in Reflected Diffusion? Conormal Trace and Reflection Masking

    Ziyue Wang, Takafumi Kanamori

    stat.ML · cs.LG

    We study score learning for reflected diffusion on bounded domains. Reflection keeps trajectories feasible but does not ensure that the learned score satisfies the boundary behavior implied by the forward process. With implicit score matching, integration by parts leaves a boundary term, and we show that it depends on one scalar at each boundary point: the diffusion- weighted normal component of the score, or conormal trace. The no-flux...

    arxiv.org/abs/2608.03469 · PDF

  5. 05

    Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models

    Edgar Jaber, Rémy Vallot, Thibault Dairay, Mathilde Mougeot

    stat.ML · cs.LG

    Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs. However, assessing the reliability of their predictions remains a major challenge, particularly in extrapolation regimes or under limited training data. In this work, we introduce a framework for quantifying model-form...

    arxiv.org/abs/2608.03360 · PDF

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