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..
Active Heteroscedastic Regression
Authors: Kamalika Chaudhuri, Prateek Jain, Nagarajan Natarajan
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude the paper presenting simulations supporting our theoretical bounds as well as experiments on real-world data. |
| Researcher Affiliation | Collaboration | Kamalika Chaudhuri 1 Prateek Jain 2 Nagarajan Natarajan 2 ... 1University of California, San Diego 2Microsoft Research, India. |
| Pseudocode | Yes | Algorithm 1 Passive Regression With Noise Oracle; Algorithm 2 Active Regression With Noise Oracle; Algorithm 3 Least Squares with Estimated Weights; Algorithm 4 Active Regression |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We also study the performance of the algorithms on two real-world datasets from UCI: (1) WINE QUALITY with m = 6500 and d = 11, and (2) MSD (a subset of the million song dataset) with m = 515345 and d = 90. |
| Dataset Splits | No | For each dataset, we create a 70-30 train-test split, and learn the best linear regressor using ordinary least squares, which forms our β . |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper describes the synthetic data generation process and data splitting for real-world datasets, but it does not specify concrete hyperparameter values or detailed training configurations for the models (e.g., learning rates, batch sizes, optimizers). |