Envy-Free Classification
Authors: Maria-Florina F. Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 5, we design and implement an algorithm that learns (almost) envy-free mixtures of linear one-vs-all classifiers. We present empirical results that validate our computational approach, and indicate good generalization properties even when the sample size is small. and All our code is included as supplementary material. Our experiments are carried out on a desktop machine with 16GB memory and an Intel Xeon(R) CPU E5-1603 v3 @ 2.80GHz 4 processor. To solve convex optimization problems, we use CVXPY [7, 1]. |
| Researcher Affiliation | Academia | Maria-Florina Balcan Machine Learning Department Carnegie Mellon University ninamf@cs.cmu.edu Travis Dick Computer Science Department Carnegie Mellon University tdick@cs.cmu.edu Ritesh Noothigattu Machine Learning Department Carnegie Mellon University riteshn@cmu.edu Ariel D. Procaccia Computer Science Department Carnegie Mellon University arielpro@cs.cmu.edu |
| Pseudocode | No | The paper describes the algorithm steps in paragraph form and uses mathematical equations for optimization problems, but does not present a structured pseudocode or algorithm block. |
| Open Source Code | Yes | All our code is included as supplementary material. |
| Open Datasets | No | In our experiments, we cannot compute the optimal solution to the original optimization problem (1), and there are no existing methods we can use as benchmarks. Hence, we rely on synthetic data. |
| Dataset Splits | No | The dataset is split into 75% training data (to measure the accuracy of our solution to the optimization problem) and 25% test data (to evaluate generalizability). The paper does not explicitly mention a validation split. |
| Hardware Specification | Yes | Our experiments are carried out on a desktop machine with 16GB memory and an Intel Xeon(R) CPU E5-1603 v3 @ 2.80GHz 4 processor. |
| Software Dependencies | No | To solve convex optimization problems, we use CVXPY [7, 1]. The paper mentions CVXPY but does not specify its version number. |
| Experiment Setup | Yes | For our experiments, we use the following parameters: |Y| = 10, q = 10, m = 5, and λ = 10.0. We set the predefined weights to be = 4, . . . , 1 2m 1 , 1 2m 1. |