stat.ML · 2026-09-09 · No. 110

Machine Learning, 2026-09-09.

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

01 — The papers

3 entries
  1. 01

    Optimal estimation for Functional Linear Regression with Noisy Discretized Data

    Sixtine Sphabmixay

    stat.ML · cs.LG

    In this paper, we consider the scalar-on-function linear regression model under a realistic sampling scheme in which the functional covariates are observed on a regular grid and contaminated by additive noise. We propose a two-step estimation procedure: first, the underlying curves are reconstructed from the discrete noisy observations using a Fourier-based projection method; second, the slope function is estimated by a penalized...

    arxiv.org/abs/2609.08671 · PDF

  2. 02

    Non-Adaptive 1-Bit Mean Estimation: Minimax Rates and the Sample-Interval Tradeoff

    Ivan Lau, Jonathan Scarlett

    stat.ML · cs.IT · cs.LG · math.ST

    We study distributed one-dimensional mean estimation under a 1-bit communication constraint. Each agent observes one sample, drawn independently from an unknown distribution, and returns a single bit in response to a query $Q: \mathbb{R}\to\{0,1\}$ chosen by a central learner. The distribution has mean in $[-λ,λ]$ and $k$-th central moment at most $σ^k$, for a fixed $k>1$. The order-optimal two-stage protocol of Lau and Scarlett uses...

    arxiv.org/abs/2609.08564 · PDF

  3. 03

    Distribution-free inference on the number of changepoints

    Rohan Hore, Aaditya Ramdas

    stat.ML · cs.LG · stat.ME

    Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$. In this paper, we study the problem of performing distribution-free inference on $K$. First, we show an impossibility result: any distribution-free upper confidence bound on $K$ must be trivial and uninformative. Then, using conformal $p$-values, and under only the assumption that the data...

    arxiv.org/abs/2609.08234 · PDF

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