Data Selection

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Overview

Data selection studies which examples, sources, labels, or interactions a model should learn from, aiming to improve performance while reducing training or annotation costs. Common approaches include coreset and diversity methods, uncertainty or active learning, gradient and influence scoring, distribution matching, and adaptive sampling policies. These methods are used in settings such as image classification, LLM instruction and preference tuning, multimodal training, and clinical labeling. Selected subsets can match or outperform full-data training, but usefulness depends on the model, target task, and budget; a key challenge is balancing short-term gains with coverage of rare cases, robustness, and long-term learning.

Latest papers

CardsList

84 papers10 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. Selective Tie Learning: Mitigating Spurious Correlations in Preference Optimization

    Oct 26, 2026Christian Moya, Purav Matlia, Elliott Thornley +1Preference OptimizationSpurious Correlation Robustness

    Purdue University · National University of Singapore

  2. When Does Data Value Reduce to Class Balance? A Coverage View of Per-Point Data Valuation

    Oct 26, 2026Yingjie Bai, Weiming ZhiData SelectionTraining Data Selection

    School of Computer Science, The University of Sydney

  3. Perturbation-Based Data Selection for Medical Question Answering

    Oct 24, 2026Yuexin Wu, Dayou Yu, Vasile RusData SelectionData Selection for Language Model Fine-Tuning

    Department of Computer Science, University of Memphis, Memphis, TN, USA

  4. ICLHerd: Herding-Based Data Selection for LLMs with In-Context Learning as a Proxy

    Oct 19, 2026Yuqiao Wen, Lili MouTraining Data SelectionData Selection

    Dept. Computing Science & Alberta Machine Intelligence Institute (Amii), University of Alberta · Canada CIFAR AI Chair

  5. Diversity Without Labels: Task-Agnostic Coreset Selection Across Perception and Action

    Oct 16, 2026Julia Hindel, Benjamin Ramtoula, Kyuhwan Yeon +4Data SelectionCoreset Selection

    University of Freiburg · University of Oxford

  6. Mind the Tail: Data Selection Through Sparse Concepts

    Oct 14, 2026Ziheng Zhang, Neha Hulkund, Colin Lee +7Training Data SelectionData Selection

    University of Texas at Austin · The Ohio State University · Massachusetts Institute of Technology +2

  7. Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility

    Oct 14, 2026Sambit Ghosh, Madelyn Mathai, R. Venkatesh Babu +1Sparse AutoencodersData-Efficient Learning

    IBM · Indian Institute of Science · University of Virginia

  8. CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives

    Oct 12, 2026Yuhan Wang, Rui Hu, Batian Di +5Semantic ClusteringInstruction-Tuning Data Selection

    Peking University · Renmin University of China · Zhejiang University +2

  9. Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization

    Oct 10, 2026Shizhan Cai, Qixin Zhang, Siyuan Ma +3Data SelectionBilevel Optimization

    College of Computing and Data Science Nanyang Technological University

  10. Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning

    Oct 4, 2026Junhui Yin, Yuchen Yang, Yilin Yin +6Class-Incremental LearningData Selection

    Beijing University of Posts and Telecommunications, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · Singapore Management University, Singapore, Singapore

  11. Training-Aware Target Coverage for Synthetic Data Selection

    Sep 30, 2026Yang Ba, Michelle V. Mancenido, Rong PanLLM Fine-TuningData Selection

    School of Computing and Augmented Intelligence, Arizona State University · School of Mathematical and Natural Sciences, Arizona State University

  12. DivOPD: Spread Wide, Look Close for Asynchronous On-Policy Distillation of Multi-turn Agents

    Sep 28, 2026Hanyang Wang, Zeyuan Liu, Zhengyu Chen +7Data SelectionOn-Policy Distillation

    University of Chicago · Meituan LongCat Interaction Team · University of the Chinese Academy of Sciences +1

  13. Refreshing Less, Selecting Better: Reusing Stale Gradient Features for Efficient Influence-Based Data Selection

    Sep 26, 2026Jianchang Su, Yifan Zhang, Wei ZhangData SelectionTraining Data Selection

    University of Connecticut

  14. OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds

    Sep 23, 2026Miroslav LžičařData SelectionVirtual Screening

    Deep MedChem

  15. GERIS: A Game-Theoretic Framework for Filtering Instance-Dependent Label Noise in License Plate Data Augmentation

    Sep 22, 2026Seyedeh Sara Jalili Shani, Rouhollah Ahmadian, Amin Rahmani +2Game-Theoretic RLData Selection

    Department of Computer Science, University of Alberta, Alberta, Canada · Department of Computer Science, University of Alberta Edmonton, Alberta, Canada · Department of Mathematics and Computer Science, Amirkabir University of Technology, Iran +1

  16. QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization

    Sep 17, 2026Yujie Li, Zezhi Shao, Chengqing Yu +7Zero-Shot Time Series ForecastingData Selection

    State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · School of Data Science and Engineering, East China Normal University, Shanghai, China

  17. An Empirical Study of Development-Tuned Representation Sensitivity for Medical QA Data Selection

    Sep 9, 2026Yuexin Wu, Dayou Yu, Vasile RusData SelectionMedical QA

    Department of Computer Science, University of Memphis

  18. Weighted Data Selection: Sharp Upper-Half and Five-Dimensional Laws

    Sep 8, 2026Zhongxuan Liu, Hongzhi WangData SelectionTraining Data Selection

    Faculty of Computing, Harbin Institute of Technology

  19. BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests

    Sep 8, 2026Jeonglyul Oh, Ikkyu Choi, Inseop Youn +1Data SelectionA/b Testing

    Dable Inc. · Seoul, Republic of Korea

  20. AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection

    Sep 5, 2026Peng Lai, He Zhu, Zhiwen Ruan +6Data SelectionPreference Alignment

    Southern University of Science and Technology · Peking University · MSRA +2

  21. A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials

    Sep 5, 2026Jia Bi, Alin-Marin ElenaTraining Data SelectionData Selection

    Science and Technology Facilities Council, Harwell Campus, Didcot, United Kingdom. · Science and Technology Facilities Council, Keckwick Lane, Daresbury, United Kingdom.

  22. Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression

    Aug 31, 2026Guangjian ZhangData SelectionTraining Data Selection

  23. Preference Data Selection for Mitigating the Alignment Tax in Large Language Models

    Aug 25, 2026Minsu Kim, Jianxun Lian, Xing Xie +1LLM AlignmentPreference Alignment

    KAIST · Microsoft Research Asia · Work done during an internship at Microsoft Research Asia

  24. Reaching the Tail: Calibration Diversity Drives Conformal Coverage under Data Scarcity

    Aug 21, 2026Donald AadithiyanRare-Event PredictionConformal Prediction

    Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka

  25. Picking the Right Image to Classify: Reliable-Input Selection in Teledermatology

    Aug 17, 2026Fabian Gröger, Marco Weishaupt, Philippe Gottfrois +6Data SelectionHealthcare

    University of Basel · Lucerne University of Applied Sciences and Arts · University Hospital Basel

  26. Test-Time Instance Selection for Improved Whole Slide Image Analysis

    Aug 14, 2026Quoc Anh Nguyen, Sunhong Park, Jin Tae KwakWhole-Slide Image AnalysisHistopathology Image Classification

    School of Electrical Engineering, Korea University, Seoul 02841, Korea

  27. Foundation Model-Enabled Efficient Data Sampling (FEEDS): A label-efficient training strategy for pan-cancer, multi-tracer PET/CT datasets

    Aug 11, 2026Biratal Raj Wagle, Bashirul Azam Biswas, Grant Chau +5Data SelectionMedical Image Segmentation

    Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Hanover, NH 03755, USA · Radiology Dartmouth Hitchcock Medical Center Lebanon, NH 03766, USA · Department of Urology Yale University New Haven, CT, USA +1

  28. Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent

    Aug 11, 2026Fanqi Zhou, Qiaosheng Chen, Zixian Huang +1Training Data CurationInstruction-Tuning Data Selection

    State Key Laboratory of Novel Software Technology, Nanjing University · Shanghai Artificial Intelligence Laboratory

  29. Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks

    Aug 10, 2026Yi Yang, Xiaoke Chen, Jinyang Huang +6Backdoor AttacksData Selection

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China · School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China · John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China +1

  30. Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation

    Jul 20, 2026Ridong Han, Yawen Shen, Zhongnian Li +3Unsupervised Domain AdaptationDomain Adaptation

    School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221116, China · Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China · Jiangsu Provincial Industrial Technology Engineering Center for Intelligent Sensing and Emergency IoT in Underground Space, Xuzhou 221116, China +1