stat.ML · 2026-07-16 · No. 55

Machine Learning, 2026-07-16.

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

    Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

    Anders Sjöberg, Nils Olsson, Marcus Baaz, Mats Jirstrand

    stat.ML · cs.LG

    Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrating these data sources within a single population modeling framework remains challenging. We extend the empirical Bayes variational autoencoder (EB-VAE) framework to joint longitudinal and time-to-event modeling and evaluate it on tumor growth data. The framework represents inter-individual...

    arxiv.org/abs/2607.13984 · PDF

  2. 02

    Parallel gradient boosting for flexible estimation of conditional distributions

    Rémy Chapelle, Nicolas Vayatis, Bruno Falissard, Mohammed Sedki

    stat.ML · cs.LG

    Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years. Among them is the prediction of entire conditional distributions rather than single functionals, which can often be framed as a multi-output regression problem, for example multiple quantile regression. Addressing such...

    arxiv.org/abs/2607.13550 · PDF

  3. 03

    Non-Expansive Two-Time-Scale Stochastic Approximation: A Fixed-Schedule One-Quarter Barrier and Bias-Corrected Acceleration

    Dhruv Sarkar, Vaneet Aggarwal

    stat.ML · cs.LG

    Non-expansive two-time-scale stochastic approximation is governed by a slow stochastic Krasnoselskii--Mann fixed-point iteration rather than by contraction to a unique equilibrium. We study this regime under a contractive fast map and a non-expansive reduced slow map. We first prove a finite-horizon lower bound showing that, for any prescribed slow stepsize schedule $(β_k)$, the classical KM residual scale $(\sum_{i<N}β_i(1-β_i))^{-1}$ is...

    arxiv.org/abs/2607.13414 · PDF

  4. 04

    Price of Fairness in Bandits: A Tight Minimax Characterization

    Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray Chowdhury

    stat.ML · cs.AI · cs.LG

    In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials. Recent work addresses this by evaluating the sequence of per-round expected rewards through the generalized $p$-mean, interpolating between utilitarian welfare ($p=1$), Nash welfare ($p\to0$), and Rawlsian fairness ($p\to-\infty$). Although tight...

    arxiv.org/abs/2607.13402 · PDF

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