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..
Provably Auditing Ordinary Least Squares in Low Dimensions
Authors: Ankur Moitra, Dhruv Rohatgi
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Applying our algorithms to the Boston Housing dataset, we exhibit regression analyses where our estimator outperforms the greedy heuristic, and can successfully certify stability even in the regime where a constant fraction of the samples are dropped. 5 EXPERIMENTS |
| Researcher Affiliation | Academia | Ankur Moitra & Dhruv Rohatgi Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | J FORMAL PSEUDOCODE FOR ALGORITHMS |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the methodology. |
| Open Datasets | Yes | On the Boston Housing dataset (Harrison Jr & Rubinfeld, 1978), we regress house values against all pairs of features. |
| Dataset Splits | No | The paper states "On the entire dataset, we find a modest positive effect..." and "On the Boston Housing dataset... we regress house values against all pairs of features," implying the full dataset was used for analysis rather than explicit train/validation/test splits for model training. |
| Hardware Specification | Yes | All experiments were done in Python on a Microsoft Surface Laptop, using GUROBI (Gurobi Optimization, LLC, 2022) with an Academic License to solve the linear programs. |
| Software Dependencies | No | The paper mentions "Python" and "GUROBI (Gurobi Optimization, LLC, 2022)" but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Heterogeneous data experiment. For each dataset, we applied the net upper bound with 1000 trials, the LP lower bound with L = { 0.01, 0, 0.01} and m = 30, and the baseline lower bound with L = { 0.01, 0, 0.01} and m = 1000. |