stat.ML · 2026-06-20 · No. 29

Machine Learning, 2026-06-20.

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

    SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data

    Jie Min, Yueyao Wang, Mengkun Chen

    stat.ML · cs.LG · stat.AP · stat.CO

    Competing risks are commonly observed in engineering fields and can bring challenges to time-to-event data modeling when the application scenarios are complicated. Recently, deep neural networks have received great attention for prediction with competing risks, due to their flexibility and high learning capability. However, the complexity of neural network structure brings extra difficulty in hyperparameter tuning based on different data...

    arxiv.org/abs/2606.20451 · PDF

  2. 02

    Statistical Properties of Training & Generalization

    Itay Lavie, Noam Levi, Yonatan Kahn

    stat.ML · cs.LG · hep-ph · physics.data-an

    Deep learning has managed to evade numerous intuitions from classical statistics to achieve unprecedented performance on a number of real-world tasks. In this article, we investigate the key features and surprises of deep learning from a physics-informed perspective, taking care to point out and justify where possible the many choices inherent in constructing a deep learning model. In particular, we review the phenomenon of neural scaling...

    arxiv.org/abs/2606.20299 · PDF

  3. 03

    Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random

    Ziheng Wei, Annie Qu, Rui Miao

    stat.ML · cs.LG

    In offline Reinforcement Learning, immediate rewards in logged batch data are often unobserved due to sparse or irregular record-keeping, or censored beyond certain reward values. This issue arises in practical settings, including health care and marketing. We investigate off-policy evaluation (OPE) in finite-horizon Markov decision processes when rewards are missing not at random (MNAR), which breaks ignorability and induces selection bias...

    arxiv.org/abs/2606.20206 · PDF

  4. 04

    Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach

    Haonan Xu, Yingying Li

    stat.ML · cs.LG · math.OC

    This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise. Existing works typically assume sub-Gaussian reward noise and bounded expected rewards, under which the optimal regret bound scales as $\tilde{O}(\sqrt{T})$ in terms of horizon $T$. However, in many applications, realized/observed rewards are also naturally bounded, implying bounded reward noise. Bounded noise is more informative than the sub-Gaussian...

    arxiv.org/abs/2606.20022 · PDF

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