Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
Authors: Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experiments on (i) fairness task with intersectional protected groups, (ii) a fairness task with noisy protected groups, and (iii) a ranking fairness task with per-query constraints. |
| Researcher Affiliation | Collaboration | Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou Google Research, USA {hnarasimhan,acotter,yichenzhou}@google.com Serena Wang, Wenshuo Guo University of California, Berkeley {serenalwang,wsguo}@berkeley.edu |
| Pseudocode | No | The paper describes algorithmic approaches in text (e.g., Section 5.1) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: https://github.com/google-research/google-research/tree/master/many_constraints. |
| Open Datasets | Yes | We use the Communities and Crime dataset [26], which contains 1,994 communities in the US described by 140 features, and seek to predict the per capita crime rate for each community. We use the UCI Adult dataset [26]. We use the Microsoft Learning to Rank Dataset (MSLR-WEB10K) [27]. |
| Dataset Splits | No | The paper explicitly mentions 'training set' and 'test set' but does not specify a 'validation set' or its split. |
| Hardware Specification | No | The paper mentions running times in Appendices D-F but does not explicitly describe the specific hardware (e.g., GPU/CPU models) used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We experiment with five multiplier architectures, ranging from under-parameterized to over-parameterized models. This includes a common multiplier for all constraints, a linear model, and neural networks with one, two and three hidden layers (with 50 nodes each). We use a one-hidden layer multiplier neural network model containing 128 nodes to assign a Lagrange multiplier for each query q Q, with the average of the feature vectors within a query used as input. The ranking model is also a one-hidden layer neural network. |