Supervised Learning

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Period ending 2026-09-14

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A weekly snapshot of new work published in Supervised Learning.

42 papers

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Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
Sep 10, 2026Toshif Khan, Muhammad AbusaqerData Poisoning Training Data
Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions
Aug 7, 2026Wei Wang, Gang Niu, Masashi SugiyamaSupervised Learning Multi-Label Classification
Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods
Jul 30, 2026J. Balibrea-Correa, E. N{á}cher, C. Fonseca-Vargas +1Spectra Supervised Learning
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
Jul 14, 2026Chengguang Gan, Zhixi Cai, Yunhao Liang +3Verifiable Rewards Learning Rates
CuBAS: Information Geometric Curvature-Based Adaptive Sampling for Supervised Classification
Jul 3, 2026Alexandre L. M. LevadaStatistical Manifold Adaptive Sampling
Gradient boosting with vector-valued leafs
Jun 28, 2026David CortesGradient Boosted Tree Decision Trees
Black-Box Assisted Regression: Phase Transitions and Minimax Optimality
Jun 24, 2026Yan ZhouRegression Empirical Risk Minimization
Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs
Jun 22, 2026Matías Gómez-Aedo, Behzad Azmi, Yuyang Huang +2Optimal Control Hessian Matrix
Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning
Jun 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorTime-Series Classification Contrastive Learning
Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
Jun 19, 2026Rajesh Mangannavar, Zachary Coalson, Pranay Dugar +1Large Language Model Planning Multi-Agent Orchestration
Subsampling for supervised learning in reproducing kernel Hilbert spaces
Jun 19, 2026Eyal Vayness, Maxime SangnierReproducing Kernel Hilbert Spaces Kernel Hilbert Spaces
Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?
Jun 13, 2026Jakob Mraz, Tomaž Curk, Blaž ZupanRelation Extraction Supervised Learning
Overhead Wildlife Locator (OWL): Benchmarking Weakly Supervised Learning for Aerial Wildlife Surveys
Jun 11, 2026Isai Daniel Chacón, Zhongqi Miao, Bruno Demuro +9Deep Learning Framework Unmanned Aerial Vehicles
Robust and sparse support vector machine via hybrid truncated loss for supervised classification
Jun 4, 2026Yuliang Yang, Chen Chen, Yuxiang Liu +1Loss Function Multi-View
Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
Jun 3, 2026Dong Li, Yapeng Cao, Haiping Zhao +1Conventional Physics-Informed Neural Networks Advection-Reaction-Diffusion
Bayes-Sufficient Representations in Supervised Learning
Jun 2, 2026Vasileios SevetlidisBayesian Inference Approaches Supervised Learning
Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Jun 1, 2026Bradley G. Karat, Maëliss Jallais, Ali R. Khan +3Signal-To-Noise Ratio Porous Media
A Fiber Criterion for Representation Identifiability in Supervised Learning
May 31, 2026Vasileios SevetlidisIdentifiability Supervised Learning
On the Generalization Gap in Self-Evolving Language Model Reasoning
May 31, 2026Zhenting Qi, Susanna Maria Baby, Stefanie Anna Baby +5Self-Evolution LLM Reasoning Methods
Generative Quantum Data Embeddings for Supervised Learning
May 29, 2026Jaewoong Heo, Daniel K. ParkSupervised Learning Generative Models
Test Time Training for Supervised Causal Learning
May 28, 2026Zizhen Deng, Jiaru Zhang, Rui Ding +5Causal Discovery Test-Time Adaptation
Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning
May 27, 2026Yuxiao Yang, Weitong ZhangGoal-Conditioned Reinforcement Learning Q-Learning
A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models
May 27, 2026Aditya Kommineni, Emily Zhou, Kleanthis Avramidis +2Electroencephalography Foundation Models Wireless Foundation Models
GS-FUSE: Granger-Supervised Gated Fusion and Multi-Granularity Alignment for Event-Driven Financial Forecasting
May 27, 2026Yang Zhang, En Chun, Ziyun Mao +2Time Series Forecasting Supervised Learning
Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning
May 21, 2026Katrin Fischer, Essence Wilson, Steffie Kim +1Technology Automation and Machines
Learning with Semantic Priors: Stabilizing Point-Supervised Infrared Small Target Detection via Hierarchical Knowledge Distillation
May 14, 2026Yuanhang Yao, Ping Qian, Zhu Liu +2Infrared Small Target Detection Feature Pyramid Networks
From Heuristics to Analytics: Forecasting Effort and Progress in Online Learning
May 12, 2026Eric S. Qiu, Danielle R. Thomas, Boyuan Guo +2Inference-Time Supervised Learning
Minimax Rates and Spectral Distillation for Tree Ensembles
May 12, 2026Binh Duc Vu, David S. WatsonRandom Forest Spectral Algorithms
Deploying Self-Supervised Learning for Real Seismic Data Denoising
May 11, 2026Giovanny A. M. Arboleda, Claudio D. T. de Souza, Carlos E. M. dos Anjos +6Seismic Interpretation Self-Supervised Learning
Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries
May 8, 2026Jiaxu Su, Junpeng Li, Changchun Hua +1Multiclass Classification Supervised Learning
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
May 8, 2026Shijun Li, Kaiwen Dong, Xiang Gao +1Large Language Model Fine-Tuning Finetuning
Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
Apr 30, 2026Mehryar Mohri, Yutao ZhongLoss Function Supervised Learning
Conformalized Super Learner
Apr 24, 2026Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez +1Conformal Prediction Nonconformity Scores
Supervised Learning Has a Necessary Geometric Blind Spot: Theory, Consequences, and Minimal Repair
Apr 23, 2026Vishal RajputAdversarial Transferability Supervised Learning
Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
Apr 22, 2026Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani +2Automated Assessment Education
Post-training is (Massive) Supervised Learning
Apr 20, 2026Michael Hassid, Yossi Adi, Roy SchwartzLarge Language Model Post-Training Post-Training
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
Mar 23, 2026Atticus Rex, Elizabeth Qian, David PetersonMulti-Fidelity Surrogate Modeling
Bound to Disagree: Generalization Bounds via Certifiable Surrogates
Feb 26, 2026Mathieu Bazinet, Valentina Zantedeschi, Pascal GermainGeneralization Bounds Empirical Risk Minimization
A Unified and Stable Risk Minimization Framework for Weakly Supervised Learning with Theoretical Guarantees
Nov 28, 2025Miao Zhang, Junpeng Li, Changchun Hua +1Supervised Learning Multiclass Classification
Beyond Correlation: Learning Supervised, Sample-Distinct, and Eigenimage-Interpretable Representations
Jul 22, 2025Mojtaba MoattariDimensionality Reduction Model Interpretability Methods
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training
Apr 2, 2025Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1Medical Image Segmentation U-Net
The Method of Gaps: Exact Expressions for the Generalization Error of Supervised Learning Algorithms
Nov 18, 2024Samir M. Perlaza, Xinying ZouGeneralization Bounds Empirical Risk Minimization

Sep 10, 2026 · cs.CRJ/K move · Enter open · S save

Empirical Evaluation of Data Poisoning Attacks in Supervised Learning

Toshif Khan, Muhammad Abusaqer

Minot State University

Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000 on both datasets and all three models while often keeping clean-test performance near baseline. The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

Data Poisoning Training Data Attack-Success Rate Supervised Learning Mnist Adversarial Attack Strategies Empirical Study

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