Linear Classification

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19 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Towards optimal selection schedules for online active learning
Oct 16, 2026Hugo Cui, Francesca Mignacco, Francesco MoriActive Learning Linear Classification
Université Paris-Saclay, CNRS, Laboratoire de mathématiques d’Orsay, 91405, Orsay, France · Joseph Henry Laboratories of Physics and Lewis–Sigler Institute, Princeton University, Princeton NJ 08544, USA · Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, 365 Fifth Ave, New York NY 10016, USA +1
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Department of Mathematics, Peking University · Department of Industrial Engineering and Operations Research, University of California, Berkeley
On the Two Faces of Adam in Separable Linear Classification
Sep 27, 2026Chen Fan, Csaba SzepesváriImplicit Bias Classification
University of Alberta
Strategic Classification Has a Missing Lever: Audit Risk
Sep 18, 2026Raman Ebrahimi, Massimo FranceschettiLinear Classification Strategic Classification
Department of Electrical and Computer Engineering, University of California, San Diego
Multiclass Linear Perceptrons with Multiplicative Margins
Aug 30, 2026Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov +2Linear Classification Multiclass Classification
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden · Institute of Information Technologies and Systems, 03187 Kyiv, Ukraine · Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia +2
Fundamental limits of distributed multiclass classification from simple binary decisions
Jul 21, 2026Ioannis Papageorgiou, Srinivas Nomula, Ayalvadi Ganesh +2Linear Classification Multiclass Classification
School of Mathematics, University of Bristol · Department of Electrical Communication Engineering, Indian Institute of Science.
Fast, Parallel, Query-Efficient Binary Classification
Jul 5, 2026Ishani Karmarkar, Liam O'Carroll, Aaron SidfordBinary Classification Classification
Stanford University
Directional Linear Separability of Neural Representations: Geometry and Transformations
Jun 7, 2026Yi Wei, Xuan Qi, Suorong Yang +1Neural Representation Geometry Linear Classification
DITEN University of Genoa, Genoa, Italy · State Key Laboratory of Novel Software Technology School of Artificial Intelligence Nanjing University, Jiangsu, China
How abundant are good interpolators?
Jun 4, 2026August Y. Chen, Ahmed El AlaouiLinear Classification Benign Overfitting
Cornell University, Department of Computer Science. Ithaca, NY, USA. · Cornell University, Department of Statistics and Data Science. Ithaca, NY, USA.
Linear Strategic Classification with Endogenous Improvements
May 31, 2026Siddharth Shrivastava, Mahvith Akshintala, B Vamsha Vardhan Reddy +3Strategic Classification Linear Classification
Department of Artificial Intelligence, IIT Hyderabad · IIIT Hyderabad · Departement of Artifical Intelligence, IIT Hyderabad
Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals
May 20, 2026Ilias Diakonikolas, Giannis Iakovidis, Mingchen MaAgnostic Learning Multiclass Classification
University of Wisconsin-Madison
Efficient and Noise-Tolerant PAC Learning of Multiclass Linear Classifiers
May 18, 2026Rita Adhikari, Shiwei ZengMulticlass Classification Classification
Augusta University
Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification
Apr 13, 2026Patrick Lutz, Themistoklis Haris, Arjun Chandra +2In-Context Learning Transformer Interpretability
Boston University, Departments of Computer Science and Electrical & Computer Engineering.
Hardness of High-Dimensional Linear Classification
Mar 19, 2026Alexander Munteanu, Simon Omlor, Jeff M. PhillipsLinear Classification Classification
TU Dortmund, Germany · University of Utah, UT, USA
On Linear Separability of the MNIST Handwritten Digits Dataset
Mar 13, 2026Ákos HajnalLinear Classification
Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN) · John von Neumann Faculty of Informatics Óbuda University Budapest, Hungary
On the Dynamics of Delayed Generalization in Linear Models
Feb 9, 2026Atreya Vedantam, Nataraj Das, Chandrashekar LakshminarayananLinear Classification Neural Network Generalization
Indian Institute of Technology Madras, Chennai.
Improved generalization bounds for binary linear classification via isoperimetry
May 22, 2025Shogo NakakitaBinary Classification Generalization Bounds
Universality of Benign Overfitting in Binary Linear Classification
Jan 17, 2025Ichiro Hashimoto, Stanislav Volgushev, Piotr ZwiernikNeural Network Generalization Linear Classification
Department of Statistical Sciences, University of Toronto
Linear and Quadratic Discriminant Analysis: Tutorial
Jun 1, 2019Benyamin Ghojogh, Mark CrowleyClassification Linear Classification
Department of Electrical and Computer Engineering, Machine Learning Laboratory, University of Waterloo, Waterloo, ON, Canada

Oct 16, 2026J/K move · Enter open · S save

Towards optimal selection schedules for online active learning

Hugo Cui, Francesca Mignacco, Francesco Mori

Université Paris-Saclay, CNRS, Laboratoire de mathématiques d’Orsay, 91405, Orsay, France · Joseph Henry Laboratories of Physics and Lewis–Sigler Institute, Princeton University, Princeton NJ 08544, USA · Initiative for the Theoretical Sciences, The Graduate Center, City University of New York, 365 Fifth Ave, New York NY 10016, USA+1

Active Learning Linear Classification Margin-Based Learning Cost-Sensitive Learning

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We consider the problem of online linear classification, where a stream of unlabeled samples is available, and annotation is costly. We investigate the optimal query policy, that prescribes whether and when a sample should be labeled and used for a training step, or instead discarded, so as to minimize a given objective. We focus on the realizable case, under Gaussian design, in the high-dimensional regime where data dimension and number of unlabeled samples grow proportionally. We consider two objectives: 1) the generalization error under a labeling budget, and 2) a cumulative cost function where mistakes have unit cost and queries cost

Δ

. We first show that the myopic policies that greedily minimize at all times the increment in the objective correspond to either small or intermediate-margin policies, that prescribe the selection of samples whose margins fall within certain intervals that shrink with time. Within this class, we numerically ascertain the optimal schedules for the selection thresholds that minimize the final values of the objectives. Taking inspiration from the myopic policy for the cumulative cost, we design a very simple query policy schedule that we compare to other proposed heuristics, on synthetic data and real medical image datasets. Finally, we discuss myopic policies for two-layer neural networks, and exemplify how naive margin-based policies can cease to be optimal.