stat.ML · 2026-08-04 · No. 74

Machine Learning, 2026-08-04.

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

    Interaction Is Not Necessary for Order-Optimal 1-Bit Mean Estimation

    Jiachen Hu, Han Zhong

    stat.ML · cs.IT · cs.LG · math.ST

    This paper is concerned with one-bit mean estimation, where each independent sample is represented by a single binary message. We consider distributions on $\mathbb{R}$ with mean in $[-λ,λ]$ and absolute $k$-th central moment at most $σ^k$, where $k>1$ is fixed. For this class, previous work attained the optimal sample complexity for general queries using a two-stage protocol. The first stage localizes the mean. The second-stage queries are...

    arxiv.org/abs/2608.02538 · PDF

  2. 02

    Computational and Statistical Guarantees of the \textit{c}-Rectified flow

    Leda Wang, Zhehao Xu, Qiang Liu, Harrison H. Zhou

    stat.ML · cs.LG · math.OC · math.PR · math.ST

    Recently, rectified flow has emerged as a fundamental framework for large-scale image generation, powering state-of-the-art systems such as FLUX.1 and Stable Diffusion 3. Despite its remarkable empirical success, the computational and statistical guarantees of iterative rectified flow have remained largely unexplored. We address this problem by studying \textit{c}-rectified flow, a cost-aware class of rectified flow that projects velocity...

    arxiv.org/abs/2608.02487 · PDF

  3. 03

    Private Generative Bootstrap via Blocking

    Jinwon Sohn, Veronika Ročková

    stat.ML · cs.LG · stat.ME

    With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers. To this end, we adopt a Bayesian likelihood-free framework and make simulation from the posterior private. In particular, we propose a new private instantiation of the Bayesian bootstrap using a blocking strategy. Rather than...

    arxiv.org/abs/2608.02480 · PDF

  4. 04

    Detecting Nonproperness of Likelihood Equations

    Xiaoxian Tang, Bican Xia, Tianqi Zhao

    stat.ML · cs.LG · cs.SC

    Given an algebraic statistical model, a challenging problem is classifying the data according to the number of positive critical points of the likelihood function. The positive critical points are the positive solutions to an algebraic system, say likelihood equations. So, identifying the number of positive critical points is a real root classification problem for the likelihood equations. A discriminant variety of a likelihood-equation...

    arxiv.org/abs/2608.01976 · PDF

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