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..
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
Authors: Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we empirically evaluate our algorithm for Massart halfspaces and find it exhibits some intriguing fairness properties. |
| Researcher Affiliation | Academia | MIT EMAIL Frederic Koehler MIT EMAIL Ankur Moitra MIT EMAIL Morris Yau UC Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1: FINDDESCENTDIRECTION(w, ε, δ, λ) and Algorithm 2: FILTERTRON(ε, η, δ, λ, T) |
| Open Source Code | Yes | All code for the experiments can be found at https://github.com/secanth/massart. |
| Open Datasets | Yes | We evaluated FILTERTRON, gradient descent on the Leaky Relu loss, logistic regression, and random forest (to compare with a less interpretable, non-halfspace classifier) on the UCI Adult dataset, obtained from the UCI Machine Learning Repository [DG17] and originally curated by [Koh96]. |
| Dataset Splits | Yes | For every p, we took a five-fold cross validation of the dataset, and for every ε ∈ [0, 0.1, 0.2, 0.3, 0.4] we repeated the following five times and took the mean: (1) randomly flip the labels for the training and test set according to the Massart adversary, (2) train on the noisy training set, and (3) evaluate according to (A) and (B). |
| Hardware Specification | Yes | The experiments on the Adult dataset were conducted in a Kaggle kernel with a Tesla P100 GPU |
| Software Dependencies | No | The paper does not provide specific software versions for its dependencies. |
| Experiment Setup | Yes | For FILTERTRON and gradient descent on the Leaky Relu loss, we ran for 2000 iterations with step size 0.05 and ε chosen by a naive grid search over [0.05, 0.1, 0.15, 0.2]. |