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..
Covariate-Powered Empirical Bayes Estimation
Authors: Nikolaos Ignatiadis, Stefan Wager
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We establish robust convergence guarantees for our method that hold under considerable generality, and exhibit promising empirical performance on both real and simulated data. |
| Researcher Affiliation | Academia | Nikolaos Ignatiadis Statistics Department Stanford University EMAIL Stefan Wager Graduate School of Business Stanford University EMAIL |
| Pseudocode | No | The paper describes the algorithm steps in numbered text but does not present them as a formal pseudocode block or algorithm figure. |
| Open Source Code | Yes | The proposed EBCF (empirical Bayes with cross-๏ฌtting) method has been implemented in EBayes.jl (https://github.com/nignatiadis/EBayes.jl) |
| Open Datasets | Yes | The Movie Lens dataset consists of approximately 20 million ratings... [Harper and Konstan, 2016]... Communities and Crimes data from the UCI repository [Dua and Graff, 2017, Redmond and Baveja, 2002] |
| Dataset Splits | Yes | We randomly choose 10% of all users and attempt to estimate the movie ratings from them. This corresponds to having a much smaller dataset. We then summarize the i-th movie, by Zi, the average of the Ni users (in the training dataset) that rated it... XGBoost [Chen and Guestrin, 2016] with number of iterations chosen by 5-fold cross-validation |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'EBayes.jl', 'MLJ.jl', 'Optim.jl', 'Distributions.jl', 'Julia language' and 'XGBoost', but does not consistently provide specific version numbers for all key software components within the text. |
| Experiment Setup | Yes | XGBoost [Chen and Guestrin, 2016] with number of iterations chosen by 5-fold cross-validation and ฮท = 0.1 (weight with which new trees are added to the ensemble) |