← Back to all news
Hugging Face Trending Papers August 31, 2026

Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression

Read the original on Hugging Face Trending Papers →

The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv Machine Learning
Sep 1

Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression

arXiv:2608.30254v1 Announce Type: new Abstract: We resolve the threshold part of Question 4 of the COLT 2025 open problem "Data Selection for Regression Tasks" of Hanneke, Moran, Shlimovich and Yehud...

By Guangjian Zhang
More like this →
arXiv Machine Learning
Aug 31

Exact Risk Ratios for Weighted Data Selection in Linear Regression

arXiv:2608. 28007v1 Announce Type: new Abstract: Hanneke, Moran, Shlimovich and Yehudayoff (COLT 2025) posed the following open problem.

By Guangjian Zhang
More like this →
arXiv AI
Aug 18

Optimal Lower Bounds for Networked Information Aggregation

arXiv:2608. 15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al.

By Ambar Pal
More like this →
Hugging Face Trending Papers
Sep 10

Thompson Sampling for Non-Monotone Convex Ridge Bandits: Monotonicity Is Not Needed for Polynomial Regret

Bakhtiari, Lattimore and Szepesvári (COLT 2025) proved that Thompson sampling (TS) has Bayesian regret $\tilde O(d^{5/2}\sqrt n)$ for bandit convex optimisation with convex \emph{monotone} ridge losse...

reinforcement-learning
More like this →
arXiv Machine Learning
Aug 21

The Price of Hidden Curvature: Improved Lower Bounds for Bandit Convex Optimization

arXiv:2607. 18652v3 Announce Type: replace-cross Abstract: We establish improved lower bounds on the minimax expected regret of stochastic bandit convex optimization for $1$-Lipschitz functions on the $d$-dimensional Euclidean ball.

By Nived Rajaraman, Yanjun Han
llmsreinforcement-learning
More like this →
Hugging Face Trending Papers
Sep 15

Sharp margin-based generalization bounds for realizable SVM

Let the exact homogeneous hard-margin support vector machine be trained on \(m\) independent observations from a Borel probability law on a real Hilbert space. We prove that, with score zero counted a...

More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea