Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Authors: Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper develops a methodology for regret minimization with stochastic first-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. |