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..
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
Authors: Constantinos Daskalakis, Ioannis Panageas
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we provide two examples/experiments, one 2-dimensional (function f : R2 R, x, y R) and one higher dimensional (f : R10 R, x, y R5). The purpose of these experiments is to get better intuition about our ๏ฌndings. |
| Researcher Affiliation | Academia | Constantinos Daskalakis CSAIL MIT Cambridge, MA 02138 EMAIL Ioannis Panageas ISTD SUTD Singapore, 487371 EMAIL |
| Pseudocode | No | The paper provides mathematical equations for the GDA and OGDA dynamics, but no pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper constructs specific polynomial functions for its examples and generates random initializations, rather than using or providing concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes using '10000 random initializations' in its experiments, but does not provide specific train/validation/test dataset splits or references to predefined splits, as it generates synthetic initial conditions rather than using a standard dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions '10000 random initializations' and uses 'ฮฑ = 0.001' for an illustration, but it does not provide comprehensive specific experimental setup details such as concrete hyperparameter values, optimizer settings, or training configurations. |