Consistent Plug-in Classifiers for Complex Objectives and Constraints
Authors: Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that our algorithm is competitive with prior methods, while being more robust to choices of hyper-parameters. We present experiments on benchmark fairness datasets and show that the proposed algorithm performs at least as well as existing methods, while being more robust to choices of hyper-parameters. |
| Researcher Affiliation | Collaboration | Shiv Kumar Tavker Indian Institute of Technology Madras, India shivtavker@smail.iitm.ac.in Harish G. Ramaswamy Indian Institute of Technology Madras, India hariguru@cse.iitm.ac.in Harikrishna Narasimhan Google Research, USA hnarasimhan@google.com |
| Pseudocode | Yes | Algorithm 1 The Split Bayes-Frank-Wolfe (SBFW) Algorithm; Algorithm 2 Plug-in Method for LMOC |
| Open Source Code | Yes | Code available at: https://github.com/shivtavker/constrained-classification. |
| Open Datasets | Yes | We ran experiments on five datasets: (1) COMPAS... (2) Communities & Crime... (3) Law School... (4) Adult... (5) Default... All experiments in this paper were carried out with publicly available datasets. [13] A. Frank and A. Asuncion. UCI machine learning repository. URL: http://archive.ics.uci. edu/ml, 2010. |
| Dataset Splits | No | We used 2/3-rd of the data for training and 1/3-rd for testing. The paper specifies a train/test split but does not mention a separate validation split or how validation was performed. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only states that 'All experiments use a linear model.' |
| Software Dependencies | No | The paper mentions software components like 'logistic regression' and 'linear model' but does not specify any version numbers for these or other software libraries/dependencies. |
| Experiment Setup | Yes | The hyper-parameters were tuned separately for each method using the heuristic of Cotter et al. (2019) [10] to trade-off between the objective and the violations. Figure 3: Robustness to hyper-parameters: Train G-mean and equal opportunity violation for six step sizes (10-4, 10-3, . . . , 10) on the COMPAS dataset. |