Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unified Sample-Optimal Property Estimation in Near-Linear Time
Authors: Yi Hao, Alon Orlitsky
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider the fundamental learning problem of estimating properties of distributions over large domains. Using a novel piecewise-polynomial approximation technique, we derive the first unified methodology for constructing sampleand time-efficient estimators for all sufficiently smooth, symmetric and non-symmetric, additive properties. This technique yields near-linear-time computable estimators whose approximation values are asymptotically optimal and highly-concentrated, resulting in the first: 1) estimators achieving the O(k/(ε2 log k)) min-max ε-error sample complexity for all k-symbol Lipschitz properties; 2) unified near-optimal differentially private estimators for a variety of properties; 3) unified estimator achieving optimal bias and near-optimal variance for five important properties; 4) near-optimal sample-complexity estimators for several important symmetric properties over both domain sizes and confidence levels. |
| Researcher Affiliation | Academia | Yi Hao Dept. of Electrical and Computer Engineering University of California, San Diego EMAIL Alon Orlitsky Dept. of Electrical and Computer Engineering University of California, San Diego EMAIL |
| Pseudocode | No | The paper describes the methodology conceptually and mathematically but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper presenting a methodology and its properties, not empirical work involving training on datasets. Therefore, there is no mention of publicly available datasets for training. |
| Dataset Splits | No | This is a theoretical paper presenting a methodology and its properties, not empirical work involving data splits for validation. Therefore, there is no mention of training/validation/test splits. |
| Hardware Specification | No | This is a theoretical paper. No specific hardware used for experiments is mentioned. |
| Software Dependencies | No | The paper mentions the 'Remez algorithm' as a computational tool for approximating polynomials, but does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper describing an estimation methodology. It does not provide details of an experimental setup such as hyperparameters or training configurations. |