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..
Learning from Label Proportions: A Mutual Contamination Framework
Authors: Clayton Scott, Jianxin Zhang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Academia | Clayton Scott and Jianxin Zhang Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI 48109 EMAIL |
| Pseudocode | Yes | Algorithm 1 Plug-in approach to LLP via LMMCM (outline) |
| Open Source Code | Yes | 2https://github.com/Z-Jianxin/Learning-from-Label-Proportions-A-Mutual-Contamination-Framework |
| Open Datasets | Yes | We consider the Adult (T = 8192) and MAGIC Gamma Ray Telescope (T = 6144) datasets (both available from the UCI repository3) |
| Dataset Splits | Yes | the parameter λ {1, 10 1, 10 2, . . . , 10 5} is chosen by 5-fold cross validation. |
| Hardware Specification | No | For each dataset, our implementation runs all 8 settings in roughly 50 minutes using 48 cores. |
| Software Dependencies | No | Our Python implementation uses Sci Py s L-BFGS routine to find the optimal αi. |
| Experiment Setup | Yes | We implement a method based on our general approach (see Algorithm 1) by taking ℓto be the logistic loss, F to be the RKHS associated to a Gaussian kernel k, and selecting f F by minimizing b Ew(f) + λ f 2 F. ... The kernel parameter is computed by 1 d V ar(X) where d is the number of features and V ar(X) is the variance of the data matrix, and the parameter λ {1, 10 1, 10 2, . . . , 10 5} is chosen by 5-fold cross validation. |