arXiv Machine Learning
Aug 11
Optimal Learning Under Tsybakov Noise
arXiv:2608. 08416v1 Announce Type: new Abstract: Probably Approximately Correct (PAC) learning [Val84] is a fundamental learning model that has been extensively investigated.
Efficiently Learning Drifting Halfspaces with Massart Noise
arXiv:2606. 11149v1 Announce Type: new Abstract: We study the problem of learning a drifting concept in the presence of Massart noise.
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arXiv Machine Learning
Sep 17
Efficient Robust Learning at the Information-Theoretic Limit
The paper presents a polynomial‑time algorithm for robustly learning Boolean concept classes with respect to a fixed distribution, achieving the optimal error rate of η + ε where η is the noise rate. It builds on Blanc’s earlier, computationally inefficient algorithm and introduces no‑regret learners to overcome the previous limitations. Additionally, the authors provide an efficient method that does not require an ERM oracle for any function class admitting sandwiching polynomials under hypercontractive distributions, including a first polynomial‑time solution for learning halfspaces with Gaussian marginals at error η + ε.
arXiv Machine Learning
Jun 16
Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension
arXiv:2602. 12471v2 Announce Type: replace Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data.
arXiv Machine Learning
Sep 3
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension
arXiv:2407. 00966v3 Announce Type: replace Abstract: In traditional models of supervised learning, the goal of a learner-- given examples from an arbitrary joint distribution on $\mathbb{R}^d \times \{\pm 1\}$-- is to output a hypothesis that is competitive (to within $\epsilon$) of the best fitting concept from some class.
arXiv Machine Learning
Jul 8
Boosting with List-Decodable Codes
arXiv:2607. 05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989).
arXiv Machine Learning
Jun 26
Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity
arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.
Hugging Face Trending Papers
Sep 21
Adversarially Robust PAC Learning with Optimal VC Rates
We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distribution over $\mathcal{X} \times \{0,1\}$, as in clas...
arXiv Machine Learning
Jun 17
On Randomized Algorithms in Online Strategic Classification
arXiv:2602. 06257v2 Announce Type: replace Abstract: Online strategic classification studies settings in which agents strategically modify their features to obtain favorable predictions.
arXiv Machine Learning
Aug 7
Optimal Rates for Learning with Monotone Adversaries
arXiv:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
arXiv Machine Learning
2d ago
Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
arXiv:2605.13684v2 Announce Type: replace Abstract: We study the optimal scale at which real-valued function classes exhibit uniform convergence and learnability. Our main result establishes a scale-...
arXiv Machine Learning
Sep 22
Adversarially Robust PAC Learning with Optimal VC Rates
arXiv:2609.24260v1 Announce Type: cross Abstract: We study the problem of \emph{adversarially robust} PAC learning. In this framework, the learner observes independent samples from an unknown distrib...
arXiv Machine Learning
Jun 30
Actively Learning Halfspaces without Synthetic Data
arXiv:2509. 20848v2 Announce Type: replace-cross Abstract: In the classic point location problem, one is given an arbitrary dataset $X \subset \mathbb{R}^d$ of $n$ points with query access to an unknown halfspace $f : \mathbb{R}^d \to \{0,1\}$, and the goal is to learn the label of every point in $X$.