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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Authors: Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,016 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,108 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper develops a methodology for regret minimization with stochastic ο¬rst-order oracle feedback in online, constrained, non-smooth, non-convex problems. Both methods are order-optimal (in the min-max sense), and we also establish a bound on the number of proximal-gradient queries these methods require. |
| Researcher Affiliation | Collaboration | Nadav Hallak 1 Panayotis Mertikopoulos 2 Volkan Cevher 3. 1Faculty of Industrial Engineering and Management, The Technion, Haifa, Israel 2Univ. Grenoble Alpes, CNRS, Inria, LIG, Grenoble, France, & Criteo AI Lab 3 Ecole Polytechnique F ed erale de Lausanne (EPFL). |
| Pseudocode | Yes | Algorithm 1: Time-smoothed online prox-grad descent, Algorithm 2: Time-smoothed online stochastic prox-grad method |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focused on developing a methodology and proving bounds; it does not report on experiments conducted using specific datasets. |
| Dataset Splits | No | This is a theoretical paper focused on developing a methodology and proving bounds; it does not report on experiments with dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper focused on developing a methodology and proving bounds; it does not report on experimental setup details like hyperparameters. |