Classification

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45 papers in the last four weeks, up 137% on the four weeks before. 0.4% of all new papers.

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Latest papers 254

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Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees
May 18, 2026Aditya Tanna, Nassim Bouarour, Mohamed Bouadi +2Tabular Foundation Models Gradient-Boosted Decision Trees
Lexsi Labs
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
May 18, 2026Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt +2Graph Neural Networks Graph-Based Semi-Supervised Learning
University of Cambridge
Evidence-Guided Unknown Rejection for High-Confidence Near-Known Unknowns
May 18, 2026Xi Chen, Yingjun Xiao, Gang FangOpen-Set Recognition Classification
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China · School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China · School of Artificial Intelligence, Guangzhou University, Guangzhou, China +3
The Neural Tangent Kernel for Classification
May 17, 2026Jonathan Plenk, Sergio Calvo-Ordonez, Alvaro Cartea +3Classification Neural Network Training Dynamics
Mathematical Institute, University of Oxford · Oxford-Man Institute of Quantitative Finance, University of Oxford · OATML, University of Oxford +2
Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries
May 15, 2026Liqi Zhou, Jiafu LiHealthcare Clinical Triage
A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions
May 14, 2026Yu Zhang, Dongjiang Zhuang, Qu Zhou +4Agentic Workflows Classification
School of Information and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China · Department of Science and Technology (Shanghai), General Administration of Customs of the P.R.C. (GACC), Shanghai, China · Nanjing Jiyun Information Technology Co., Ltd., Nanjing, China +2
Focused PU learning from imbalanced data
May 14, 2026Elias Zavitsanos, Georgios PaliourasClass-Imbalanced Learning Binary Classification
Institute of Informatics and Telecommunications, NCSR “Demokritos”, Patriarhou Gregoriou E and 27 Neapoleos St., Agia Paraskevi, 15310, Attica, Greece.
Reducing cross-sample prediction churn in scientific machine learning
May 13, 2026Gordan Prastalo, Kevin Maik JablonkaEnsemble Learning Classification
Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Hahn-Meitner-Platz 1, 14109 Berlin, Germany · HIPOLE Jena (Helmholtz Institute for Polymers in Energy Applications Jena), Lessingstrasse 12–14, 077432026 Jena, Germany · Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstrasse 10, 07743 Jena, Germany +2
Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
May 13, 2026Nicola Debole, Andrea Passerini, Stefano Teso +2Concept Bottleneck Models Human-in-the-Loop Annotation
DISI, University of Trento, Italy · CIMeC, University of Trento, Italy
Classifier Context Rot: Monitor Performance Degrades with Context Length
May 12, 2026Sam Martin, Fabien RogerLanguage Model Safety Evaluation Long-Context Language Model Evaluation
Anthropic Fellows Program · Anthropic
How Useful Is Cross-Domain Generalization for Training LLM Monitors?
May 12, 2026Sam Martin, Fabien RogerSupervised Fine-Tuning Cross-Domain Generalization
Anthropic Fellows Program · Anthropic
Choosing features for classifying multiword expressions
May 12, 2026Eric LaporteText Classification Classification
Université Paris-Est, Laboratoire d’informatique Gaspard-Monge CNRS UMR 8049, F77454 Marne-la-Vallée, France
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
May 12, 2026Phu-Hoa Pham, Chi-Nguyen Tran, Nguyen Lam Phu Quy +3Class-Incremental Learning Continual Learning
Faculty of Information and Technology University of Science Vietnam National University Ho Chi Minh City, Vietnam · Department of Computer Science University of Warwick Coventry, United Kingdom
DANCE: Detect and Classify Events in EEG
May 11, 2026Jarod Lévy, Hubert Banville, Jérémy Rapin +3Electroencephalography Brain-Computer Interfaces
1Meta AI · 2Inria, Université Paris-Saclay, Palaiseau, France
Deep Arguing
May 11, 2026Adam Gould, Francesca ToniNeural Network Interpretability Computational Argumentation
Department of Computing Imperial College London
Multi-domain Multi-modal Document Classification Benchmark with a Multi-level Taxonomy
May 11, 2026Denghao Ma, Qing Liu, Zulong Chen +5Hierarchical Classification Text Classification
Beijing Information Science and Technology University, China · Alibaba Group, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), China +2
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
May 10, 2026Lin Li, Jiawei Huang, Qihao Quan +7Multivariate Time Series Classification Time Series Classification
Sun Yat-sen University Zhuhai, Guangdong, China · Xiaomi Corporation Beijing, China · University of Science and Technology of China Hefei, Anhui, China +1
Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
May 9, 2026Weidong Zheng, Kongyang Chen, Yuanwei Guo +1Classification Machine Unlearning
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510335, China · School of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China · Pazhou Lab, Guangzhou 510006, China +1
Black-box model classification under the discriminative factorization
May 8, 2026Hayden Helm, Merrick Ohata, Carey PriebeClassification Black-Box Model Auditing
Helivan · JHU
Flexible Routing via Uncertainty Decomposition
May 8, 2026Charlotte Peale, Siddartha Devic, Parikshit Gopalan +2Uncertainty Quantification Adaptive Model Routing
1Stanford University · University of Southern California · 3Apple
In-Context Fixation: When Demonstrated Labels Override Semantics in Few-Shot Classification
May 8, 2026Ming LiuLLM Interpretability Few-Shot Learning
Amazon
Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries
May 8, 2026Jiaxu Su, Junpeng Li, Changchun Hua +1Multiclass Classification Weakly Supervised Learning
Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, China
When Symbol Names Should Not Matter: A Logistic Theory of Fresh-Symbol Classification
May 8, 2026Wenjie Guan, Jelena BradicTransformer Classification
Department of Statistics and Data Science, Cornell University
Empirical Evidence for Simply Connected Decision Regions in Image Classifiers
May 7, 2026Arjhun Swaminathan, Mete AkgünClassification Image Classification
Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML), University of Tübingen. · Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen.
When Labels Have Structure: Improving Image Classification with Hierarchy-Aware Cross-Entropy
May 7, 2026April Chan, Davide D'Ascenzo, Sebastiano Cultrera di MontesanoHierarchical Classification Fine-Grained Image Classification
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA · Department of Computer Science, University of Milan, Milan, Italy · Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy +1
A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
May 5, 2026Sushovan Majhi, Atish Mitra, Žiga Virk +1Point Cloud Classification Classification
Data Science, George Washington University, USA · Department of Mathematical Sciences, Montana Technological University, USA · Faculty of Computer and Information Science, University of Ljubljana, Slovenia +1
The Manokhin Probability Matrix: A Diagnostic Framework for Classifier Probability Quality
May 5, 2026Valery ManokhinPost-Hoc Calibration Probability Calibration
Independent Researcher
Imbalanced Classification under Capacity Constraints
May 5, 2026Daniel Fraiman, Ricardo FraimanClass-Imbalanced Learning Binary Classification
Departamento de Matem´atica y Ciencias, Universidad de San Andr´es, Buenos Aires, Argentina · CONICET, Argentina. · PEDECIBA, Matem´atica, Uruguay.
Instance-Level Costs for Nuanced Classifier Evaluation
May 4, 2026Kabir Kang, Stephen MussmannCost-Sensitive Learning Classification
School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA.
A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification
May 4, 2026Sushovan Majhi, Atish Mitra, Žiga Virk +1Point Cloud Classification Classification
Data Science, George Washington University, USA · Department of Mathematical Sciences, Montana Technological University, USA · Faculty of Computer and Information Science, University of Ljubljana, Institute IMFM, Slovenia +1

May 18, 2026, cs.LGJ/K move · Enter open · S save

Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees

Aditya Tanna, Nassim Bouarour, Mohamed Bouadi, Vinay kumar Sankarapu+1

Lexsi Labs

Tabular Foundation Models Gradient-Boosted Decision Trees Cost-Aware Inference Classification Teacher-Student Learning Tabular ICL+1

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A fraud scorer needs to answer in under 2 ms. The best tabular foundation models (TFMs) take 151-1,275 ms on GPU. We close this gap by distilling the TFM offline into an XGBoost or CatBoost student that runs natively on CPU. The central obstacle is specific to in-context learning (ICL) teachers: they leak labels when scoring their own training set, so the soft targets collapse to near-one-hot vectors with no inter-class structure left to distill. Stratified out-of-fold (OOF) teacher labeling prevents this. Across 153 classification datasets drawn from TALENT, OpenML-CC18, TabZilla, and TabArena, distilling TabICLv2 into XGBoost gives 0.882 macro-mean AUC (96.5% of teacher AUC) at 1.9 ms on CPU, a 38x to 860x speedup across teacher-student pairs with a statistically significant edge over a tuned CatBoost baseline (Wilcoxon p = 0.0008; 51% win rate). Four further findings: teacher rank transfers exactly to student rank; gains concentrate on low-dimensional data (< 21 features: +0.011 over CatBoost vs. >21 features: +0.001); multi-teacher averaging helps MLP students (+0.006, p = 0.003) but adds less than 0.001 for tree students; and on high-dimensional tasks where the teacher itself trails CatBoost, distillation makes things worse rather than better. The full pipeline is open-sourced as part of the TabTune library.

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Latest papers 254