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..
Optimal Estimation of High Dimensional Smooth Additive Function Based on Noisy Observations
Authors: Fan Zhou, Ping Li
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical simulation results are presented to validate our analysis and show its superior performance of the proposed estimator over the plug-in approach in terms of bias reduction and building confidence intervals. |
| Researcher Affiliation | Industry | Fan Zhou, Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St Bellevue WA 98004 USA EMAIL |
| Pseudocode | No | The paper describes the estimator and its components mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper describes generating its own synthetic data for simulations rather than using a publicly available or open dataset. It states: 'The unknown parameters 2 Rd are randomly generated that yield a uniform distribution over [0.2, 0.4]d for different dimension parameter d.' and 'The distribution of " we use is " N(0, σ2Id).' |
| Dataset Splits | No | The paper describes numerical simulations with data generation and independent runs to estimate expectations, variance, and MSE, but it does not specify traditional training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, processor types, memory) used for running its numerical simulations. |
| Software Dependencies | No | The paper mentions the use of 'MATLAB built-in function histfit() and fitdist()' but does not provide specific version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | We set σ = 1 and n = 104. For the dimension factor, we set d = n and ranges from 0.5 to 0.95 with an incremental size 0.05. The distribution of " we use is " N(0, σ2Id). The additive function we use has a homogeneous H older structure: f( ) := Pd j . We set sample size n = 104 and N = 102 to approximate (7.2). |