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..
Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements
Authors: Kimon Antonakopoulos, Panayotis Mertikopoulos
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Fig. 1, we report the performance of the (non-adaptive) entropic gradient descent and proportional response algorithms studied by Birnbaum et al. [10], and we compare it to the performance of ADAMIR, which consistently outperforms both methods, in terms of both last-iterate and ergodic value convergence rates. We provide a more detailed analysis in the paper s supplement. In the supplement, we also perform a numerical validation of the method in the context of a Fisher market model. |
| Researcher Affiliation | Collaboration | Kimon Antonakopoulos Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG 38000 Grenoble, France & Criteo AI Lab EMAIL EMAIL |
| Pseudocode | No | The paper describes the ADAMIR method using mathematical equations and definitions but does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper conducts a case study on 'Fisher markets' where 'marginal utilities drawn i.i.d. at each epoch' are used, implying a simulated environment. It does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes experiments on a simulated Fisher market but does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In Fig. 1, we report the performance of the (non-adaptive) entropic gradient descent and proportional response algorithms... in a stochastic Fisher market with marginal utilities drawn i.i.d. at each epoch. The marked lines are the observed means from S = 50 realizations, whereas the shaded areas represent a 95% confidence interval. |