stat.ML · 2026-07-22 · No. 61

Machine Learning, 2026-07-22.

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

    Fundamental limits of distributed multiclass classification from simple binary decisions

    Ioannis Papageorgiou, Srinivas Nomula, Ayalvadi Ganesh, Sidharth Jaggi, Parimal Parag

    stat.ML · cs.IT · cs.LG · math.ST

    We consider the problem of constructing a $K$-class classifier from the combination of $O(\log K)$ simple binary classifiers -- this is a natural paradigm to construct a sophisticated classifier in a distributed manner with each agent performing a relatively straightforward task. We study the fundamental performance limits of such a classifier when the corresponding binary classifiers are hyperplanes. For a stylized Gaussian setting where the...

    arxiv.org/abs/2607.19334 · PDF

  2. 02

    The Tractability Landscape of Sampling with Inexact Scores

    Anming Gu, Kevin Tian, Hubert Yang, Yusong Zhu

    stat.ML · cs.LG · math.ST

    We provide a simple and tight characterization of the types of inexact score oracle access that permit sampling with vanishing total variation bias, for a standard, well-behaved target family. Our main result shows that any weaker error than the sub-Gaussian assumption used by [YW26] rules out the tractability of unbiased sampling. This strengthens the conclusion of [CCSW26] to be algorithm-agnostic, and to hold for a wider range of error assumptions.

    arxiv.org/abs/2607.19004 · PDF

  3. 03

    Algebraic Signatures for Structural Learning in Probability Tensors

    Akihiro Maeda, Shohei Hidaka, Satoshi Aoki

    stat.ML · cs.LG

    Algebraic statistics characterizes statistical models through polynomial constraints, but it has mainly been used for analytically specified model classes. This paper studies the inverse problem: identifying probabilistic structure from vanishing binomials observed in empirical probability tensors. We treat the vanishing binomials of a toric model as its algebraic signature, and turn the ideal-variety correspondence of algebraic statistics...

    arxiv.org/abs/2607.18817 · PDF

  4. 04

    The Price of Hidden Curvature: An $\widetildeΩ (d^{5/4} \sqrt{T})$ Lower Bound for Bandit Convex Optimization

    Nived Rajaraman

    stat.ML · cs.IT · cs.LG

    We establish a $\widetildeΩ(d^{5/4}\sqrt T)$ lower bound on the minimax expected regret of stochastic bandit convex optimization of $1$-Lipschitz functions on the Euclidean ball. This presents the first nontrivial regret lower bound that grows faster than $d\sqrt{T}$ for this problem, establishing that stochastic bandit convex optimization is fundamentally harder than linear bandits. The hard class of convex functions we construct takes the...

    arxiv.org/abs/2607.18652 · PDF

  5. 05

    Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics

    Vignesh Tirukkonda, Gautam Dasarathy

    stat.ML · cs.LG · math.ST

    Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process. We study exact recovery of the graph from one trajectory of random-scan Gaussian Glauber dynamics. Existing techniques for this problem either inherit the mixing time of the chain, which can be super-polynomial in the dimension $p$ without strong assumptions, or are...

    arxiv.org/abs/2607.18559 · PDF

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