stat.ML · 2026-10-06 · No. 135

Machine Learning, 2026-10-06.

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

01 — The papers

7 entries
  1. 01

    Direct Intermediate Initialization for Tilted Diffusion Samplers

    Gregory D. Bellchambers

    stat.ML · cs.LG

    Some diffusion posterior samplers construct Gaussian-tilted intermediate distributions along the reverse process. We observe that these targets can be pulled back to clean-space posteriors with weaker conditioning, with samples transported analytically to the corresponding noisy-space target through a Gaussian bridge. For the sequential Monte Carlo (SMC) sampler MCGDiff, the effective observation variance of this pulled-back problem is up to...

    arxiv.org/abs/2610.06834 · PDF

  2. 02

    A Solvable Model of Adaptive Learning Rate Rescaling: Acceleration, Stability & Scaling

    Itay Lavie, Clarissa Lauditi, Cengiz Pehlevan

    stat.ML · cond-mat.dis-nn · cs.LG

    A recurring design principle in modern optimizers is to decouple update magnitude from the raw gradient norm, yet its consequences for learning-curve and resource scaling remain unclear. We isolate this mechanism by studying normalized SGD in a random-feature model with power-law teacher and data covariance. Fixed-norm updates induce an effective learning rate that grows as gradients shrink. We derive a dynamical mean-field theory (DMFT)...

    arxiv.org/abs/2610.06701 · PDF

  3. 03

    Inverse Cross-spectral Neural Networks for Multivariate Time Series

    Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa

    stat.ML · cs.LG · eess.SP · stat.ME

    CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statistics. These architectures, however, are designed for independent and identically distributed observations and do not fully capture the joint structure of temporal and cross-variable dependencies in multivariate time series. In this work, we introduce Inverse...

    arxiv.org/abs/2610.06630 · PDF

  4. 04

    The Surrogate Is Not the Reward: Post-Surrogate Primary-Outcome Acquisition in Contextual Bandits

    Kyungbok Lee, Michael R. Kosorok

    stat.ML · cs.LG

    We study contextual bandits in which a surrogate is observed after the action but before the learner decides whether to acquire the primary outcome that defines action value and regret. The value of acquiring the primary outcome depends on both decision relevance (how much the current outcome matters for comparing policies) and the residual uncertainty after observing the surrogate. The Audited Surrogate Bandit (ASB) learns a contextual...

    arxiv.org/abs/2610.06610 · PDF

  5. 05

    KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction

    Rahul Goswami

    stat.ML · cs.LG

    Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies. Among the various models available, the Survival Forest has demonstrated significant effectiveness in numerous scenarios. In this work, we propose an ensemble method that leverages the strengths of the Survival Forest as the master model, complemented by several base models. This ensemble...

    arxiv.org/abs/2610.06434 · PDF

  6. 06

    Valid Stopping in Adaptive Generator-Verifier Loops

    Mahmoud Hegazy, Michael I. Jordan, Aymeric Dieuleveut

    stat.ML · cs.AI · cs.LG · stat.ME

    Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow terminates when a proposal is verified as good enough. The verifier typically proxies a more costly ground-truth oracle, and as the generator searches adaptively against it, false acceptances may accumulate. Proposals can pass the proxy but fail under the costlier ground-truth check. We study when...

    arxiv.org/abs/2610.06432 · PDF

  7. 07

    Latent Similarity Gaussian Processes: A Theory-Grounded Approach to Personalized Suicide-Risk Forecasting for Clinical Decision-Support

    Yaniv Yacoby, Weiwei Pan, Hope Neveux, Taylor C. McGuire, Franchesca Castro-Ramirez, Anushka R. Patel, Matthew K. Nock

    stat.ML · cs.LG

    Forecasting suicide risk is difficult due to the high heterogeneity of patients and the low base rate of suicide-related events (SREs). We present Latent Similarity Gaussian Processes (LSGPs), which embed patients in a continuous latent space to jointly model similarity and forecast risk. By selectively drawing information from latent peers, LSGPs better capture individualized risk trajectories, generalizing nomothetic (pooled), idiographic...

    arxiv.org/abs/2610.06355 · PDF

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