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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
What is a Good Metric to Study Generalization of Minimax Learners?
Authors: Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we aim to answer this question by first showing that primal risk, a universal metric to study generalization in minimization, fails in simple examples of minimax problems. Furthermore, another popular metric, the primal-dual risk, also fails to characterize the generalization behavior for minimax problems with nonconvexity, due to non-existence of saddle points. We thus propose a new metric to study generalization of minimax learners: the primal gap, to circumvent these issues. Next, we derive generalization bounds for the primal gap in nonconvexconcave settings. |
| Researcher Affiliation | Academia | Massachusetts Institute of Technology University of Maryland, College Park |
| Pseudocode | Yes | These two algorithms are described in Algorithms 1 and 2 in Appendix E. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. It mentions "numerical verified in the literature" but does not offer its own implementation. |
| Open Datasets | No | The paper uses analytical and theoretical examples (e.g., 'Example 1 (Analytical example)', 'Example 2 (GAN-training example)'). While it references concepts from GANs and cites relevant papers, it does not provide access information for a specific public dataset used for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments requiring training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations. |