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..
Generalized Smooth Variational Inequalities: Methods with Adaptive Stepsizes
Authors: Daniil Vankov, Angelia Nedich, Lalitha Sankar
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present numerical experiments that support our theoretical guarantees and highlight the efficiency of proposed adaptive stepsizes. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Arizona State University Tempe, Arizona, USA. |
| Pseudocode | Yes | Algorithm 1 Korpelevich Method with Backtracking |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | We present the experiments on training GAN for the 2D Ring dataset, a mixture of 8 equal-prior Gaussian distributions, with mean cos(2πi/8), sin(2πi/8) for i {1, . . . , 8} and variance 10 4. The paper describes how the dataset was generated but does not provide access information (link, DOI, formal citation) to a publicly available version of this dataset. |
| Dataset Splits | No | The paper mentions running methods for 100 epochs with a batch size of 128 but does not provide explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory specifications, or types of computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | We set parameters of the problem to be {(α 0.090., p = 2.1), (α 0.66, p = 4.0), (α 0.86, p = 8.0)}. We run methods for 100 epochs with a batch size of 128. γk = 10 3 for methods without clipping, and γk = 10 3 min{1, 1 F (hk) } for methods with clipping. Korpelevich method with clipping and backtracking (Algorithm 1) with β = 1.0 and q = 0.75. γk = βk min{1, 1 F (hk) , 1 ( uk hk 1 +1)α/(1 α) }, with the same βk = a b+k for all methods, where a = b = 100. |