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..
Convex and Non-convex Optimization Under Generalized Smoothness
Authors: Haochuan Li, Jian Qian, Yi Tian, Alexander Rakhlin, Ali Jadbabaie
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we further generalize this non-uniform smoothness condition and develop a simple, yet powerful analysis technique that bounds the gradients along the trajectory, thereby leading to stronger results for both convex and non-convex optimization problems. In particular, we obtain the classical convergence rates for (stochastic) gradient descent and Nesterov s accelerated gradient method in the convex and/or non-convex setting under this general smoothness condition. |
| Researcher Affiliation | Academia | Haochuan Li MIT EMAIL Jian Qian* MIT EMAIL Yi Tian MIT EMAIL Alexander Rakhlin MIT EMAIL Ali Jadbabaie MIT EMAIL |
| Pseudocode | Yes | Algorithm 1: Nesterov s Accelerated Gradient Method (NAG) and Algorithm 2: NAG for µ-strongly-convex functions |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not perform empirical evaluations using datasets. Therefore, it does not discuss dataset availability for training. |
| Dataset Splits | No | This is a theoretical paper that does not perform empirical evaluations using datasets. Therefore, it does not provide details on training/validation/test splits. |
| Hardware Specification | No | This is a theoretical paper presenting mathematical analysis and proofs of convergence rates. It does not describe any computational experiments or hardware specifications. |
| Software Dependencies | No | This is a theoretical paper focusing on mathematical analysis and algorithm design (pseudocode). It does not mention any specific software dependencies or their versions. |
| Experiment Setup | No | This is a theoretical paper that does not conduct experiments. Therefore, it does not describe experimental setup details such as hyperparameters or training settings. |