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..
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization
Authors: Kfir Levy, Ali Kavis, Volkan Cevher
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we propose STORM+, a new method that is completely parameter-free, does not require large batch-sizes, and obtains the optimal O(1/T 1/3) rate for finding an approximate stationary point. Our work builds on the STORM algorithm, in conjunction with a novel approach to adaptively set the learning rate and momentum parameters. |
| Researcher Affiliation | Academia | Kfir Y. Levy Technion EMAIL Ali Kavis EPFL EMAIL Volkan Cevher EPFL EMAIL |
| Pseudocode | Yes | We describe our method in Alg. 1 and Eq. (8), and state its guarantees in Theorem 1. For completeness we present our method in Alg. 1 |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. It focuses purely on theoretical contributions and algorithmic development without any implementation details or links. |
| Open Datasets | No | This is a theoretical paper and does not involve experiments or the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve experiments or the use of datasets, thus no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments or implementations requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and convergence analysis. It does not describe any experimental setup details such as hyperparameters or training configurations. |