Adaptive Sampling

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Overview

Adaptive sampling studies how to choose which examples, measurements, or computational steps to spend a limited budget on. Common approaches use uncertainty, prediction error, information gain, or difficulty to guide active learning, Bayesian experimental design, bandit allocation, and adaptive numerical solvers; learned policies, including reinforcement learning, are also used. Applications range from scientific experiments and sensor acquisition to diffusion-model sampling and test-time reasoning with language models. A central challenge is ensuring that the signals used to prioritize samples reliably improve the final task without sacrificing coverage, introducing bias, or costing more to compute than they save.

Latest papers

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202 papers37 papers in the last four weeks, up 106% on the four weeks before. 0.2% of all new papers.

  1. BFMT: Enhancing Search Capabilities of Tree Sampler via Bootstrap Flow-Map Tree

    Jul 3, 2026Binglin Ji, Anindya Sarkar, Hengchang Lu +4Inference-Time SearchImage Generation

    Department of CSE, Washington University in St.Louis, USA · Department of Information Technology, Uppsala University, Sweden

  2. Adaptive Reparametrized Time for Score-Based Diffusion Sampling

    Jul 2, 2026Yilie Huang, Wenpin Tang, Xun Yu ZhouDiffusion SamplingAdaptive Sampling

    Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong. · Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. · Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University, New York, NY 10027, USA.

  3. Accelerated sampling using SamAdams variable timesteps and position-adaptive Langevin dynamics

    Jun 25, 2026Benedict Leimkuhler, Peter A. WhalleyLangevin DynamicsPosterior Sampling

    School of Mathematics, University of Edinburgh, Edinburgh, UK. · Seminar for Statistics, Department of Mathematics, ETH Zürich, Zürich, Switzerland.

  4. QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification

    Jun 23, 2026Parth Upman, Shreyank N GowdaClass-Imbalanced LearningSynthetic Data Augmentation

    School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, United Kingdom

  5. Dynamic estimation of slowly varying sequences

    Jun 22, 2026Prashant Gokhale, Mikhail Khodak, Sandeep SilwalAdaptive Sampling

    University of Wisconsin–Madison

  6. FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments

    Jun 22, 2026Jinwoo Choi, Yeonkyu Lee, Jung-Taak Kim +2Robot NavigationAdaptive Sampling

    Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea

  7. Ranking-and-Selection with Multiple Correct Answers and Non-Answerable Estimates

    Jun 20, 2026Qiaoqiao Wang, Wei YouBest-Arm IdentificationAdaptive Sampling

    Dept. of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Hong Kong, China

  8. Adaptive Beam Selection for Efficient Scanning Probe Tomography

    Jun 19, 2026San Dinh, Zichao Wendy Di, Matt MenickellyImage ReconstructionSequential Experimental Design

    Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA

  9. Subsampling for supervised learning in reproducing kernel Hilbert spaces

    Jun 19, 2026Eyal Vayness, Maxime SangnierImportance SamplingAdaptive Sampling

    Sorbonne Universit´e, LPSM, Paris, France

  10. CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes

    Jun 18, 2026Zhijian Zhou, Zesheng Ye, Zhaorun Chen +2LLM EvaluationAnytime-Valid Inference

    The University of Melbourne · Department of Computer Science, University of Chicago · Department of Computer Science, University of Illinois Urbana-Champaign

  11. Video2Code: Generating Interactive Webpages from UI Videos via Action-Aware Revisit

    Jun 16, 2026Mingde Xu, Zhen Yang, Yan Wang +7Web Application GenerationAdaptive Sampling

    University of Waterloo · Tsinghua University · Zhipu AI +1

  12. Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

    Jun 15, 2026Qitian Lu, Jafar Jafari-Asl, Panagiotis Spyridis +1Surrogate ModelingUncertainty Quantification

    Brno University of Technology, Brno, Czech Republic · University of Rostock, Rostock, Germany

  13. Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels

    Jun 15, 2026Kirill Korolev, Nikita Morozov, Stepan Pavlenko +2Markov Chain Monte CarloBayesian Inference

    1HSE University · University of Edinburgh · 3CIFAR Fellow.

  14. A Complexity Measure for Active Learning in Multi-group Mean Estimation

    Jun 12, 2026Abdellah Aznag, Rachel Cummings, Adam N. ElmachtoubMulti-Armed BanditsActive Learning

    Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University

  15. Adaptive Nucleus Truncation for Long-Form Reasoning

    Jun 12, 2026Ousmane Amadou DiaLanguage Model DecodingAdaptive Sampling

  16. RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning

    Jun 10, 2026Atif Hassan, Swanand Khare, Jiaul H. PaikDataset PruningTraining Data Selection

    Department of Artificial Intelligence, IIT Kharagpur, Kharagpur, West Bengal, India · Department of Mathematics, IIT Kharagpur, Kharagpur, West Bengal, India

  17. Annealed Entropic Allocation for Ranking and Selection

    Jun 9, 2026Xin Fei, Juergen BrankeMulti-Armed BanditsAdaptive Sampling

    Business School · The University of Edinburgh, Edinburgh, UK · Warwick Business School +1

  18. Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation

    Jun 9, 2026Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu +1Adaptive SamplingLLM Post-Training

    Lexsi Labs

  19. Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

    Jun 8, 2026Pierre Cesar, Sofya Dymchenko, Abhishek Purandare +1PDE Surrogate ModelingActive Learning

    DATAMOVE · Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, Grenoble, France

  20. sGPO: Trading Inference FLOPs for Training Efficiency in RLVR

    Jun 7, 2026Shivchander Sudalairaj, Kai Xu, Akash Srivastava +1Group Relative Policy OptimizationAdaptive Sampling

    AI Innovation, Red Hat · Core AI, IBM

  21. Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling

    Jun 7, 2026Hong Guo, Nianhui Guo, Christoph Meinel +1Markov Chain Monte CarloInference-Time Scaling

    Hasso Plattner Institut · German University of Digital Science

  22. Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals

    Jun 6, 2026Lingkai Kong, Hezi Jiang, Andrew Ma +3Adaptive SamplingModel-Based Planning

    Harvard University

  23. DAS-PINNs for high-dimensional partial differential equations: extending deep adaptive sampling to spacetime domains

    Jun 4, 2026Anshima Singh, David J. SilvesterPDE SolvingAdaptive Sampling

    Department of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK

  24. Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events

    Jun 4, 2026Rishal Aggarwal, David Ryan Koes, Nicholas M. Boffi +1Adaptive SamplingMolecular Dynamics Simulation

    CMU-Pitt Program in Computational Biology Dept. of Computational & Systems Biology University of Pittsburgh · Machine Learning Department Dept. of Mathematical Sciences Carnegie Mellon University · Courant Institute, New York University Machine Learning Lab +1

  25. Revisiting Prototype Rehearsal for Exemplar-Free Continual Learning: Manifold-Aware Boundary Sampling with Adaptive Class-Balanced Loss

    Jun 4, 2026Hongye Xu, Bartosz KrawczykClass-Incremental LearningPrototype-Based Classification

    Chester F. Carlson Center for Imaging Science Rochester Institute of Technology

  26. Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

    Jun 2, 2026Joanna Zou, Fraser Birks, Dallas Foster +1Machine Learning Interatomic PotentialsActive Learning

    Center for Computational Science & Engineering, Schwarzman College of Computing, MIT · Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick · NVIDIA

  27. A Quantitative Approximation Framework for Flow Distillation in Diffusion Models

    Jun 2, 2026Weiguo Gao, Ming Li, Lei Shi +1Diffusion Model DistillationFew-Step Diffusion Sampling

    School of Mathematical Sciences, Fudan University, Shanghai, 200433, China · Shanghai Key Laboratory of Contemporary Applied Mathematics, Fudan University, Shanghai, 200433, China

  28. Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling

    Jun 2, 2026Runpeng Dai, Tong Zheng, Rui Liu +2Reinforcement LearningTest-Time Scaling

    University of North Carolina at Chapel Hill · University of Maryland, College Park · Washington University in St. Louis

  29. Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing

    Jun 1, 2026Arda Fazla, Abolfazl HashemiDebiased MLTraining Data Selection

    School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.

  30. Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

    Jun 1, 2026Louise Davy, Stephan Clémençon, Charlotte LaclauPairwise ComparisonImportance Sampling

    IDS, LTCI Télécom Paris Palaiseau, France