Provably Auditing Ordinary Least Squares in Low Dimensions
Authors: Ankur Moitra, Dhruv Rohatgi
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 {moitra, drohatgi}@mit.edu |
| 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. |