Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Matrix Estimation for Individual Fairness
Authors: Cindy Zhang, Sarah Huiyi Cen, Devavrat Shah
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 23,621 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,630 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify these results on synthetic and real data. ... We empirically verify these results on real and synthetic datasets. In Section 6, we demonstrate our findings on synthetic data and the Movie Lens 1M dataset. |
| Researcher Affiliation | Academia | Cindy Y. Zhang * 1 Sarah H. Cen * 2 Devavrat Shah 2 *Equal contribution 1Princeton University 2Massachusetts Institute of Technology. |
| Pseudocode | No | The paper describes the steps of Singular Value Thresholding (SVT) in numbered prose, but it does not present them in a structured pseudocode or algorithm block format with a clear label. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a specific repository link, an explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | Yes | We empirically verify these results on real and synthetic datasets. ...Movie Lens 1M dataset (Harper & Konstan, 2015) |
| Dataset Splits | Yes | Out of the observed entries (i, j) Ω, 80 percent are used for training and the remaining 20 percent are used for validation; the unobserved entries form our test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software components like 'deep neural net' and 'K-nearest neighbors algorithm' but does not specify any library or solver names with version numbers. |
| Experiment Setup | Yes | We use a batch size of 128 and 2000 steps of training. |