Sample Size

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3 papers in the last 28 days · 0.0% of indexed attention

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Period ending 2026-09-14

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A Statistical Approach to Estimating Sample Size of Machine Learning Models
Sep 10, 2026Dat Phan-Trong, Sunil Gupta, Svetha VenkateshSample Size Machine Learning
A Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning
Sep 9, 2026Lingxiao Qu, Yan PeiKernel Method Principal Component Analysis
Length Generalization for Transformers via Compression
Sep 8, 2026Georg Zetzsche, Hongjian Jiang, Andy Yang +4Transformer Architectures Generalization Bounds
A Systematic Sample Size Analysis of ML-Based Path Loss Prediction for LPWAN
Aug 11, 2026Robert Bitterling, Christian Nettersheim, Jörn Hees +1Propagation Sample Size
Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift
Aug 6, 2026Hoang Dang, Luan Pham, Minh NguyenHeterogeneous Treatment Effects Bayesian Experimental Design
Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
Jul 16, 2026Mahmoud Ibrahim, Bart Elen, Chang Sun +2Medical Image Generation Medical Image Classification
Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Jul 7, 2026Kisung YouImportance Sampling Wasserstein Distance
Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
Jun 26, 2026Stephan J. Lehmler, Tobias Glasmachers, Ioannis IossifidisSurface Electromyography Memorization
Estimating Uncertainty in Classifier Performance with Applications to Large Language Models and Nested Data
Jun 24, 2026Kylie AnglinConfidence Intervals Sample Size
The price of multi-group transductive learning
Jun 3, 2026Noah Bergam, Samuel Deng, Daniel HsuTransductive Active Labeling Multiclass Classification
Finding Needles in the Haystack: Transductive Active Labeling in Ecology
Jun 2, 2026Rupa Kurinchi-Vendhan, Sara BeeryActive Learning Transductive Active Labeling
Sample-Size Scaling of the African Languages NLI Evaluation
Jun 2, 2026Anuj Tiwari, Oluwapelumi Ogunremu, Terry Oko-odion +2Legal Natural Language Processing Benchmarks Multilingual Language Models
Repetition Mismatch: Why Data Mixture Experiments Don't Scale and How to Fix Them
May 29, 2026Kevin Zhou, Lisa Alazraki, Kris Cao +1Repetition Mixtures
The Privacy Price of Tail-Risk Learning: Effective Tail Sample Size in Differentially Private CVaR Optimization
May 15, 2026El Mustapha MansouriConditional-Value-At-Risk Long-Tailed Distribution
Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage
May 11, 2026Prasanjit Dubey, Xiaoming HuoBandwidth Constraints Sample Size
How Much is Brain Data Worth for Machine Learning?
May 10, 2026Lane Lewis, Zhixin Wang, David Schwab +1Machine Learning Sample Size
Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
May 9, 2026Ankit Hemant Lade, Sai Krishna Jasti, Indar Kumar +1Bottlenecks Sample Size
Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
May 9, 2026YongKyung Oh, Henry W. Zheng, Jeffrey Feng +1Survey Machine Learning
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
May 5, 2026Kisung YouMarkov Chain Monte Carlo Sample Size
RuC: HDL-Agnostic Rule Completion Benchmark Generation
Apr 30, 2026Arnau Ayguadé Domingo, Miquel Alberti-Binimelis, Cristian Gutierrez-Gomez +5Hardware Description Languages Sample Size
Unstable Rankings in Bayesian Deep Learning Evaluation
Apr 25, 2026Qishi Zhan, Minxuan Hu, Guansu Wang +2Sample Size Deep Learning
Fast estimation of Gaussian mixture components via centering and singular value thresholding
Apr 21, 2026Huan QingGaussian Mixture Models High-Dimensional Data
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
Apr 19, 2026Paul M. ThompsonScaling Laws Sample Size
Covariance-Based Structural Equation Modeling in Small-Sample Settings with $p>n$
Apr 18, 2026Hiroki Hasegawa, Aoba Tamura, Yukihiko OkadaStructural Equation Models Covariance Estimation
Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
Dec 16, 2025Aaron Wei, Milad Jalali, Danica J. SutherlandGeneralized U-Statistics Maximum Mean Discrepancy
Multiple Testing of Linear Forms for Noisy Matrix Completion
Dec 1, 2023Wanteng Ma, Lilun Du, Dong Xia +1Tensor Completion False Discovery Rate

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

A Statistical Approach to Estimating Sample Size of Machine Learning Models

Dat Phan-Trong, Sunil Gupta, Svetha Venkatesh

Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and effect structure to be specified a priori. Nonlinear ML models learn complex prediction surfaces that do not admit straightforward analytical power calculations. We propose a framework that approximates nonlinear ML models with localized linear representations and estimates sample size requirements by evaluating statistical power across these local regions.

Sample Size Machine Learning Statistical Inference Power Method

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