Margin-Based Learning

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13 papers0 papers in the last four weeks, against 1 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
Low-Quality Face Recognition using Center Aligned Representations and Local Margin Constraints
Sep 1, 2026Vedat Can Dilaver, Benjamin S. RigganMargin-Based Learning Low-Rank Adaptation
University of Nebraska Lincoln
Sum-of-Squares Degree Barriers for the Reweighted-Hinge Method in Robust Halfspace Learning: A Christoffel-Function Characterization
Jun 15, 2026Xiaoyu LiCorruption Robustness Noise Robustness
University of New South Wales
Efficiently Learning Drifting Halfspaces with Massart Noise
Jun 9, 2026Mingchen Ma, Guyang Cao, Jelena Diakonikolas +1Noisy-Label Learning Low-Degree Polynomial Method
University of Wisconsin-Madison
Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning
May 13, 2026Najwa Laabid, Vikas GargMargin-Based Learning Retrosynthesis
Department of Computer Science Aalto University · YaiYai Ltd
Spoken Language Identification with Pre-trained Models and Margin Loss
May 3, 2026Zhihua Fang, Liang He, Weiwu JiangLanguage Identification Margin-Based Learning
School of Computer Science and Technology, Xinjiang University, Urumqi, China · 4Central R&D Department, AGIBOT, Shanghai, China · School of Intelligence Science and Technology, Xinjiang University, Urumqi, China +1
Too Sharp, Too Sure: When Calibration Follows Curvature
Apr 22, 2026Alessandro Morosini, Matea Gjika, Tomaso Poggio +1Loss Landscape Sharpness Margin-Based Learning
Massachusetts Institute of Technology, Cambridge, MA, USA.
Tight Bounds for Learning Polyhedra with a Margin
Apr 16, 2026Shyamal Patel, Santosh VempalaMargin-Based Learning PAC Learning
Columbia University · Georgia Tech
Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise
Apr 7, 2026Yuanjie Shi, Peihong Li, Zijian Zhang +2Margin-Based Learning Learning with Noisy Labels
School of EECS, Washington State University, Pullman, WA, USA
Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training
Mar 27, 2026Roberto Sprengel Minozzo Tomchak, Oge Marques, Lucas Garcia Pedroso +2Metric Learning Margin-Based Learning
Departamento de Informática, Universidade Federal do Paraná, Curitiba, Brazil · College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, U.S.A · Departamento de Matemática, Universidade Federal do Paraná, Curitiba, Brazil
Margin in Abstract Spaces
Mar 7, 2026Yair Ashlagi, Roi Livni, Shay Moran +1Kernel Methods Statistical Learning Theory
School of Electrical and Computer Engineering, Tel Aviv University. · Departments of Mathematics, Computer Science, and Data and Decision Sciences, Technion, and Google Research. · Departments of Mathematics, Technion.
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
Aug 6, 2024Jinghui Yuan, Weijin Jiang, Zhe Cao +4Margin-Based Learning Ensemble Learning
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, P. R. China · School of Automation, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, P. R. China · Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China
Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension
Jul 1, 2024Gautam Chandrasekaran, Adam Klivans, Vasilis Kontonis +2Agnostic Learning Margin-Based Learning
a University of Texas at Austin · b University of California, Los Angeles

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.