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..
Small-loss Adaptive Regret for Online Convex Optimization
Authors: Wenhao Yang, Wei Jiang, Yibo Wang, Ping Yang, Yao Hu, Lijun Zhang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we first propose a novel algorithm that achieves a small-loss adaptive regret bound for exp-concave and smooth function. Subsequently, to address the limitation that existing algorithms can only handle one type of convex functions, we further design a universal algorithm capable of delivering small-loss adaptive regret bounds for general convex, exp-concave, and strongly convex functions simultaneously. That is challenging because the universal algorithm follows the metaexpert framework, and we need to ensure that upper bounds for both meta-regret and expert-regret are of small-loss types. Moreover, we provide a novel analysis demonstrating that our algorithms are also equipped with minimax adaptive regret bounds when functions are non-smooth. |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2School of Artificial Intelligence, Nanjing University, Nanjing 210023, China 3Xiaohongshu Inc., Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Follow-the-Leading-History for Smooth functions (FLHS) Algorithm 2 A Universal Algorithm for Exploiting Smoothness to Improve the Adaptive Regret (USIA) Algorithm 3 Expert Esp: Online Newton Step (ONS) Algorithm 4 Expert Esp: Smooth and Strongly Convex OGD (S2OGD) Algorithm 5 Expert Esp: Scale-free online gradient descent (SOGD) Algorithm 6 Expert-algorithms for USIA Algorithm 7 An Improved Implementation of USIA Algorithm 8 Expert-algorithms for improved USIA |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and theoretical bounds for online convex optimization. It does not use or mention any datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about experimental setup, hyperparameters, or training configurations. |