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..
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
Authors: Eduard Gorbunov, Marina Danilova, Alexander Gasnikov
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several numerical experiments with the proposed methods in order to justify the theory we develop. In particular, we show that clipped-SSTM can outperform SGD and clipped-SGD in practice even without using large batchsizes. |
| Researcher Affiliation | Academia | Eduard Gorbunov MIPT and HSE, Russia Marina Danilova ICS RAS and MIPT, Russia Alexander Gasnikov MIPT and HSE, Russia |
| Pseudocode | Yes | Algorithm 1 Clipped Stochastic Similar Triangles Method (clipped-SSTM) |
| Open Source Code | Yes | One can find the code here: https://github.com/eduardgorbunov/accelerated_clipping. |
| Open Datasets | Yes | We have tested4 clipped-SSTM and clipped-SGD on the logistic regression problem, the datasets were taken from LIBSVM library [4]. |
| Dataset Splits | No | The paper mentions using datasets from LIBSVM library but does not explicitly state the training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | To implement methods we use Python 3.7 and standard libraries. |
| Experiment Setup | Yes | For all methods we used constant batchsizes m, stepsizes and clipping levels were tuned, see Section H.2 for the details. |