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.
Monotone Classification with Relative Approximations
arXiv:2506. 10775v3 Announce Type: replace Abstract: In monotone classification, the input is a multi-set $P$ of points in $\mathbb{R}^d$, each associated with a hidden label from $\{-1, 1\}$.
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arXiv Machine Learning
Aug 11
Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers
arXiv:2605. 18662v2 Announce Type: replace Abstract: Noise-tolerant PAC learning of linear models has been of central interests in machine learning community since the last century.
arXiv Machine Learning
Jul 17
Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
arXiv:2607. 14889v1 Announce Type: new Abstract: This paper studies an optimal linear combination of binary classifiers based on a logical structuration of the dataset via truth tables.
arXiv Machine Learning
Jun 5
Robust and sparse support vector machine via hybrid truncated loss for supervised classification
arXiv:2606. 05814v1 Announce Type: new Abstract: The support vector machine (SVM) is a widely used classifier, but choosing an appropriate loss function remains difficult.
arXiv Machine Learning
Jun 10
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.
arXiv Machine Learning
Jul 21
An Iterative Geometric Approach to Optimizing Separating Hyperplanes
arXiv:2607. 17282v1 Announce Type: new Abstract: Given a binary-labeled linearly separable dataset, and the objective is to compute the maximum-margin separating hyperplane, also known as the hard-margin Support Vector Machine (SVM) classifier.
arXiv Machine Learning
Jul 21
The Label Complexity of Class-Conditional Coverage under Distribution Shift
arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.
arXiv Machine Learning
Jun 15
On Rate-Optimal Partitioning Classification from Observable and from Privatised Data
arXiv:2312. 14889v4 Announce Type: replace-cross Abstract: In this paper we revisit the classical method of partitioning classification and prove novel convergence rates under relaxed conditions, both for observable (non-privatised) and for privatised data.
arXiv Machine Learning
Jun 2
Robust Learning of a Group DRO Neuron
arXiv:2601. 18115v2 Announce Type: replace Abstract: We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional shifts, for a broad family of covariate distributions.
arXiv Machine Learning
Jun 30
Multiplicative Oracle Inequalities for Transductive Learning via Level-Set Aggregation
arXiv:2603. 02043v2 Announce Type: replace Abstract: We revisit transductive learning where predictions are made with the set of all covariates known in advance.
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 $X$.
arXiv AI
Aug 7
An Optimal Agnostic PAC Algorithm
arXiv:2608. 06363v1 Announce Type: cross Abstract: Let $H\subseteq\{-1,+1\}^X$ be a class of finite VC dimension $d\ge1$.