arXiv Machine Learning June 10, 2026 By Gabriel Ponte, Marcia Fampa, Jon Lee

The hyper-scaled NLP bound for maximum-entropy remote sampling

Read the original on arXiv Machine Learning →

arXiv:2601. 20970v3 Announce Type: replace-cross Abstract: The maximum-entropy remote sampling problem (MERSP) is to select a subset of $s$ random variables from a set of $n$ random variables, so as to maximize the information concerning a set of target random variables that are not directly observable.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

2d ago

Dimension-Free Rank Lifting from Random Hyperplane Arrangements

arXiv:2609.39855v1 Announce Type: new Abstract: We study the width required for a randomly initialized hidden layer of a neural network to achieve rank lifting. Namely, given a dataset $X \in \mathbb...

By Luca Becchetti, Matteo Russo, Ruben Skorupinski

arXiv AI

Jun 2

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation

arXiv:2606. 00703v1 Announce Type: cross Abstract: Low-precision pretraining (FP8, MXFP4, NVFP4) is now standard for frontier language models, yet the literature is almost entirely achievability -- algorithms and empirical scaling laws -- with no matching characterization of what is information-theoretically possible.

By Munsik Kim

llms efficiency

arXiv Machine Learning

Aug 18

Spectral Certificates and Projection-DPP Rounding for Determinantal MAP Selection

arXiv:2606. 19411v3 Announce Type: replace Abstract: Selecting a fixed-size subset that maximizes the determinant of a positive semidefinite kernel is the MAP problem for a size-constrained determinantal point process and the classical maximum-entropy sampling problem.

By Richard Yi Da Xu

arXiv Statistics ML

Sep 16

Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model

arXiv:2606.07124v2 Announce Type: replace-cross Abstract: We study the minimax estimation error for distributed covariance matrix estimation in the vertical-split (feature-split) setting, where two a...

By Jing Yee Tan, Guangyue Han

agents efficiency

arXiv Machine Learning

Jun 4

Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond

arXiv:2604. 08438v2 Announce Type: replace Abstract: The Shapley value, and its broader family of semi-values, has received much attention in various attribution problems.

By Weida Li, Yaoliang Yu, Bryan Kian Hsiang Low

arXiv Machine Learning

Jul 21

Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization

arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.

By Ria Stevens, Fangshuo Liao, Barbara Su, Thanasis Hadjidimoulas, Jianqiang Li, Anastasios Kyrillidis

benchmarks

One email a morning, machine-written

Related stories

Dimension-Free Rank Lifting from Random Hyperplane Arrangements

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation

Spectral Certificates and Projection-DPP Rounding for Determinantal MAP Selection

Information-Theoretic Bounds for Sparse Covariance Estimation in the Vertical-Split Distributed Model

Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond

Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization