Uncertainty Sampling

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics
Sep 23, 2026Zheyu Zhu, Junchao Zhu, Fengbei Liu +7Spatial Transcriptomics Active Learning
University of Pennsylvania, Philadelphia, PA 19104, USA · Vanderbilt University, Nashville, TN, USA · Cornell Tech, New York, NY 10044, USA +4
Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers
Sep 1, 2026Giovanni Bonetta, Matteo Merler, Davide Zago +2Uncertainty Sampling Visual RL
Department of Computer Science, University of Torino, Torino, Italy · Natural Language Processing Unit, Fondazione Bruno Kessler, Trento, Italy
Spending Scarce Confirmatory PET Measurements: Target-Aligned Validation in A4/LEARN
Aug 23, 2026Eliuvish Han Cui, Qiang Yang, Meredith Mengmeng ZhangAdaptive Sampling Uncertainty Sampling
Clinical Functional Service Provider (cFSP) Department, Kuntuo, an IQVIA company · Department of Biostatistics, University of California, Los Angeles · Department of Biometrics, Hengrui Pharmaceuticals
Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction
Jul 30, 2026Gousia Habib, Laura Ruotsalainen3D Reconstruction Neural Rendering
Department of Computer Science, University of Helsinki, Finland
One-step lowest-variance selection in a Gaussian random-field model motivated by masked diffusion: Total correlation and a square root collision threshold
Jul 20, 2026Linjun LiMasked Diffusion Models Parallel Decoding
Department of Mathematics, University of Pennsylvania, Philadelphia, PA.
Adaptive Diversity-Uncertainty Active Learning with Redundancy Control for Bioacoustic Event Classification
Jul 6, 2026Gabriel Dubus, Hugo Magaldi, Anatole Gros-MartialMulti-Label Classification Active Learning
Eco-Anthropologie, Mus´eum National d’Histoire Naturelle, UMR7206, CNRS, Paris, France · Centre d’Etudes Biologiques de Chiz´e, UMR 7372, CNRS, La Rochelle Universit´e, France
Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
Jul 1, 2026Eliott Thomas, Mickael Coustaty, Aurelie Joseph +3Table Structure Recognition Document Information Extraction
L3i, La Rochelle Université, La Rochelle, France · Yooz, Aimargues, France
CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes
Jun 18, 2026Zhijian Zhou, Zesheng Ye, Zhaorun Chen +2LLM Evaluation Anytime-Valid Inference
The University of Melbourne · Department of Computer Science, University of Chicago · Department of Computer Science, University of Illinois Urbana-Champaign
Uncertainty-Aware Budget Allocation for Adaptive Test-Time Reasoning
May 26, 2026Manh Nguyen, Sunil Gupta, Hung LeEfficient Language Model Reasoning Adaptive Sampling
Applied Artificial Intelligence Initiative Deakin University, Australia
OnceSelect: Reusable Data Selection for Efficient Multimodal Instruction Tuning
May 26, 2026Mingkang Dong, Muxin Pu, Hongyi Cai +5Instruction-Tuning Data Selection Multimodal Large Language Models
Faculty of Computer Science Universiti Malaya Kuala Lumpur, Malaysia · School of Information Technology Monash University Malaysia Subang Jaya, Selangor, Malaysia · Faculty of Computer Science University of Science and Technology Beijing Beijing, China
Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
May 21, 2026Vipul Arya, S. H. Shabbeer Basha, Srikrishna U N +2Active Learning Uncertainty Sampling
School of Computer Science and Engineering, RV University, Bengaluru, Karnataka-560059, India · School of Engineering & Technology, Vidyashilp University, Bangalore, India · Shiv Nadar Institution of Eminence, Delhi NCR
LAPS: Improving Incremental LiDAR Mapping using Active Pooling and Sampling for Neural Distance Fields
May 15, 2026Dongjae Lee, Wooseong Yang, Yifu Tao +2Simultaneous Localization and Mapping Replay-Based Continual Learning
Department of Mechanical Engineering, Seoul National University, S. Korea · Oxford Robotics Institute at the University of Oxford, UK
Portable Active Learning for Object Detection
May 11, 2026Rashi Sharma, Justin Timothy C. Bersamin, Karthikk SubramanianActive Learning Uncertainty Sampling
Panasonic R&D Center Singapore · Nanyang Technological University
Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking
May 11, 2026Riku Inoue, Shogo Sato, Kazuhiko Murasaki +3Active Learning Uncertainty Sampling
NTT, Inc., Kanagawa, Japan
UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
Apr 28, 2026Jongyoon Kim, Minseong Hwang, Seung-won HwangUnsupervised Domain Adaptation Domain Adaptation
Interdisciplinary Program in Artificial Intelligence, Seoul National University
Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
Mar 26, 2026Jiahao Wu, Ning Lu, Shengcai Liu +6RL for Language Models Sample-Efficient RL
Southern University of Science and Technology · The Hong Kong Polytechnic University · The Hong Kong University of Science and Technology +3

Sep 23, 2026, cs.CVJ/K move · Enter open · S save

Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

Zheyu Zhu, Junchao Zhu, Fengbei Liu, Tianyuan Yao+6

University of Pennsylvania, Philadelphia, PA 19104, USA · Vanderbilt University, Nashville, TN, USA · Cornell Tech, New York, NY 10044, USA+4

Spatial Transcriptomics Active Learning Training Data Selection Uncertainty Sampling Spatial Gene Expression Prediction

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Spatial transcriptomics (ST) measures gene expression in tissue context, but dense capture grids can be costly and may repeatedly sample morphologically similar regions. Most active learning strategies were developed for categorical labels and independent samples. We conduct a retrospective pool-based benchmark of active learning versus uniform Random sampling for ST, where expression vectors are high-dimensional and continuous and candidates are spatially correlated. Using two fully profiled public ST cohorts, we mask candidate expression vectors and simulate multi-round selection with uncertainty-based Monte Carlo dropout (MC-dropout) and temporal output discrepancy (TOD), and diversity-based CoreSet and TypiClust-inspired selection. We compare 160 completed configurations at 5%, 10%, 30%, and 50% of the fold-wide training spot pool under patient-level cross-validation, with a separate full-label reference. Within each budget, strategies share the selection schedule, morphology-to-expression predictor, and optimization protocol. We assess mean per-gene within-slide Pearson correlation coefficient (PCC), expression-cluster agreement, and Moran's I fidelity. On HER2-positive breast cancer, pooled mean PCC differences from Random across the four active strategies were -0.0176, -0.0117, +0.0056, and +0.0057 at 5%, 10%, 30%, and 50%, respectively. On cutaneous squamous cell carcinoma (cSCC), three strategies were below Random at 5%, and all four were below Random at 10%. On HER2-positive breast cancer, CoreSet and MC-dropout had lower PCC but higher expression-cluster agreement than Random at the two smallest budgets; this pattern did not reproduce on cSCC. Under the reported fixed training horizons, the evaluated active strategies do not consistently improve on Random at small budgets, and rankings depend on the evaluation measure.

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