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..
Learning A Minimax Optimizer: A Pilot Study
Authors: Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments on a variety of minimax problems corroborate the effectiveness of Twin-L2O. We benchmark our algorithms on several testbed problems and compare against state-of-the-art minimax solvers. |
| Researcher Affiliation | Collaboration | Jiayi Shen1*, Xiaohan Chen2*, Howard Heaton3*, Tianlong Chen2, Jialin Liu4 , Wotao Yin3,4 , Zhangyang Wang2 1Texas A&M University, 2University of Texas at Austin, 3University of California, Los Angeles, 4Alibaba US, Damo Academy |
| Pseudocode | Yes | The full method is outlined in the Method 1, where the L2O update is denoted by LSTM(uk; φk) and the fallback method is a Halpern iteration (Halpern, 1967). |
| Open Source Code | Yes | The code is available at: https://github.com/VITA-Group/L2O-Minimax. |
| Open Datasets | No | The paper describes generating instances for specific problem formulations (Seesaw, Rotated Saddle, Matrix Game) by sampling parameters and initializing variables, rather than using a pre-existing publicly available dataset. For example: "we use 128 optimizee instances for training; each of them has its parameters i.i.d. sampled, and variables x, y randomly initialized by i.i.d. sampling from U[ 0.5, 0.5]." |
| Dataset Splits | Yes | A validation set of 20 optimizees is used with parameters and variables sampled in the same way; and similarly we generate a hold-out testing set of another 100 instances. |
| Hardware Specification | Yes | All experiments in this and following sections are conducted using the Ge Force GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper mentions software components like LSTM and Adam optimizer but does not specify version numbers for any libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used for implementation. |
| Experiment Setup | Yes | The L2O training routine follows (Andrychowicz et al., 2016): we use 128 optimizee instances for training; each of them has its parameters i.i.d. sampled, and variables x, y randomly initialized by i.i.d. sampling from U[ 0.5, 0.5]. For each epoch, an L2O optimizer will update the optimizee parameters for 1000 iterations, with its unrolling length T = 10. When the next epoch starts, all x, y as well as LSTM hidden states are reset. We train the L2O solvers for 200 epochs, using Adam with a constant learning rate 10 4. |