Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability
Authors: Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 sitanc@mit.edu Frederic Koehler MIT fkoehler@mit.edu Ankur Moitra MIT moitra@mit.edu Morris Yau UC Berkeley morrisyau@berkeley.edu |
| 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]. |