Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities
Authors: Eduard Gorbunov, Adrien Taylor, Gauthier Gidel
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 20,883 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,951 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By solving (10) numerically, we empirically observe that e GPEG(γ, L, N) = O(1/N) for different choices of γ, see Fig. 1a. ... Codes for verifying the potentials and convergence rates are publicly available: https://github.com/eduardgorbunov/potentials_ and_last_iter_convergence_for_VIPs, the codes rely on the PEP packages [Taylor et al., 2017b, Goujaud et al., 2022] as well as on YALMIP [Lofberg, 2004]. |
| Researcher Affiliation | Academia | Eduard Gorbunov MIPT, Russia Mila & Ude M, Canada MBZUAI, UAE EMAIL; Adrien Taylor INRIA & École Normale Supérieure, CNRS & PSL Research University, France EMAIL; Gauthier Gidel Mila & Ude M, Canada Canada CIFAR AI Chair EMAIL |
| Pseudocode | No | The paper describes the methods using mathematical recursions and equations (e.g., Proj-EG, Proj-PEG, OG) but does not provide a formally labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Codes for verifying the potentials and convergence rates are publicly available: https://github.com/eduardgorbunov/potentials_ and_last_iter_convergence_for_VIPs |
| Open Datasets | No | The paper focuses on theoretical analysis and numerical verification using performance estimation problems, not on traditional machine learning experiments that involve training on a specific dataset. Therefore, no public dataset is referenced for training. |
| Dataset Splits | No | The paper focuses on theoretical analysis and numerical verification using performance estimation problems, not on traditional machine learning experiments that involve training/validation splits. |
| Hardware Specification | No | The paper mentions using SDP solvers and YALMIP for numerical experiments but does not specify any hardware details like CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions using 'the PEP packages' and 'YALMIP' but does not specify their version numbers, which is necessary for reproducibility. |
| Experiment Setup | Yes | Under Assumption 1, for all N 0 and γ = 1/3L, we have F(x N) 2... Under Assumption 1, for all N 2 the iterates of Proj-PEG with γ = 1/4L satisfy... |