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 [1].
Satisfying Real-world Goals with Dataset Constraints
Authors: Gabriel Goh, Andrew Cotter, Maya Gupta, Michael P. Friedlander
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both benchmark and real-world industry datasets demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | Gabriel Goh Dept. of Mathematics UC Davis Davis, CA 95616 EMAIL Andrew Cotter, Maya Gupta Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043 EMAIL EMAIL Michael Friedlander Dept. of Computer Science University of British Columbia Vancouver, B.C. V6T 1Z4 EMAIL |
| Pseudocode | Yes | Algorithm 1 Proposed majorization-minimization procedure for (approximately) optimizing Problem 2... Algorithm 2 Skeleton of a cutting-plane algorithm that optimizes Equation 6... |
| Open Source Code | Yes | Our publicly-available Julia implementation3 for these experiments uses LIBLINEAR [11] with the default parameters... 3https://github.com/gabgoh/svmc.jl |
| Open Datasets | Yes | We compare training for fairness on the Adult dataset 2, the same dataset used by Zafar et al. [27]. ... 2 a9a from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html |
| Dataset Splits | Yes | The 32 561 training and 16 281 testing examples, derived from the 1994 Census... For all three datasets, we split out 80% for training and reserved 20% for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'Julia implementation' and 'LIBLINEAR' but does not specify their version numbers in the main text. |
| Experiment Setup | Yes | Our publicly-available Julia implementation3 for these experiments uses LIBLINEAR [11] with the default parameters (most notably λ = 1/n 3 10 5) to implement the SVMOptimizer function, and does not include an unregularized bias b. ... We performed 5 iterations of the majorization-minimization procedure of Algorithm 1. ... We chose the regularization parameter λ using a power-of-10 grid search, found that 10 7 was best for this baseline, and then used λ = 10 7 for all experiments. |