Stratified Sampling

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10 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning
Oct 13, 2026Christian Sanchez-Leon, Dongming Chen, Dongqi WangStratified Sampling Training Data Curation
Software College, Northeastern University, Shenyang, 110819, China
AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis
Oct 9, 2026Natapong Nitarach, Phume Ngampornsukswadi, Pittawat Taveekitworachai +3Stratified Sampling Anti-Money Laundering Detection
SCB DataX, SCBX Group · Independent Researcher
LLM Judge Validation Under Sparse Overlap: From Inference to Design
Sep 25, 2026Junxuan Li, Arko Mukherjee, Soumyabrata PalInter-Rater Reliability LLM Evaluation
Adobe · Adobe Research
Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments
Sep 20, 2026Tomoka Takei, Shunnosuke Ikeda, Yuichi TakanoStratified Sampling Variance Reduction
University of Tsukuba, Tsukuba-shi, Ibaraki 305-8573, Japan
Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift
Aug 6, 2026Hoang Dang, Luan Pham, Minh NguyenDistribution Shift Robustness Causal Effect Estimation
Independent Researcher · University of New South Wales, Australia · Florida Atlantic University, USA
Stochastic Emulation using Generalized Stratified Sampling for Performance-Based Risk Optimization of Structures
Aug 5, 2026Isabela D. Rodrigues, Seymour M. J. Spence, Henrique M. Kroetz +1Surrogate Modeling Uncertainty Quantification
Department of Structural Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil · Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, 48109, Michigan, USA · Center for Marine Studies, Federal University of Paraná, Pontal do Paraná, 13566-590, Paraná, Brazil
Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization
Jul 12, 2026Fabrizio Marozzo, Stefano IannicelliText Summarization Stratified Sampling
University of Calabria, Rende, Italy
Variance Reduction for Expectations with Diffusion Teachers
May 20, 2026Jesse Bettencourt, Xindi Wu, Matan Atzmon +2Diffusion Model Distillation Stratified Sampling
1NVIDIA · University of Toronto · 3Princeton University
The Partition Principle Revisited: Non-Equal Volume Designs Achieve Minimal Expected Star Discrepancy
Feb 27, 2026Xiaoda XuNumerical Integration Stratified Sampling
School of Mathematics and Physics, Suqian University
Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling
Feb 22, 2026Norrakith Srisumrith, Sunantha SodseeStratified Sampling Explainable Artificial Intelligence
Dept. of Digital Network and Information Security Management King Mongkut’s University of Technology North Bangkok Bangkok, Thailand

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Deficit-Driven Stratified Acquisition for Building STRATA-S12: An Open SAR-Optical Corpus for Self-Supervised Learning

Christian Sanchez-Leon, Dongming Chen, Dongqi Wang

Software College, Northeastern University, Shenyang, 110819, China

Stratified Sampling Training Data Curation Remote Sensing Remote Sensing Benchmarks

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Building a co-registered SAR-optical corpus for self-supervised Earth Observation is above all an acquisition problem, yet the main multi-modal datasets are collected opportunistically, with no per-stratum quota control and no mechanism against the geographic dropout that non-uniform cloud cover leaves behind. We present Deficit-Driven Stratified Acquisition (DDSA), an algorithm that treats building a co-registered Sentinel-1/Sentinel-2 corpus as a constrained maximum-coverage problem over a hemisphere-corrected (class x season x region) stratification, enforces strict temporal pairing and a multi-stage quality gate, and closes the loop during acquisition with a human-in-the-loop deficit-repair stage. Measured on its own product, the algorithm yields three results a single-pass acquisition cannot report. Deficit-driven ordering makes a partial run a covering one: it holds every one of the 192 strata populated from the first few percent of the budget, where an abundance-first order leaves most strata empty until the end. Whether a failed attempt can be recovered depends on the temporal persistence of its cause, not the kind of defect: geometric failures such as nodata are among the most repairable (eta = 41.4%) while cloud is among the least (eta = 18.8%), because a cloudy location is cloudy on most dates of its season. The repair loop supplies 39.5% of the released pairs at no measurable quality cost, with 89.8% surviving manual review. The quality guarantee has a price: 2,928,370 acquisition attempts produced 740,746 released pairs. We release the corpus, STRATA-S12, with STAC/PDS4-aligned FAIR sidecars, and a pre-registered linear-probe check confirms it is fit for self-supervised pre-training. A metadata-level coverage comparison against SSL4EO-S12 and SEN12MS situates the corpus over a shared frame; systematic cross-corpus and cross-method comparison is left to follow-up work.