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..
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability
Authors: Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Let d denote the dimensionality and PT the path length of comparators that reflects the environmental non-stationarity. We demonstrate that an exponential-weight method with fixed-share updates achieves an O(d T 1/3P 2/3 T log T) dynamic regret for mixable losses, improving upon the bestknown O(d10/3T 1/3P 2/3 T log T) result (Baby & Wang, 2021) in d. More importantly, this improvement arises from a simple yet powerful analytical framework that exploits the mixability, which avoids the Karush-Kuhn-Tucker-based analysis required by existing work. Theorem 1. Under Assumption 1, 2 and 3, for any ut W, Algorithm 1 with µ = 1/T ensures D-REGT ({ut}T t=1) O d log T (1 + T 1/3P 2/3 T ). Theorem 3. Under Assumptions 1, 4 and 5, Algorithm 3 with µ = 1/T ensures D-REGT O d log T (1 + T 1/3P 2/3 T ) for any comparator sequence u1, . . . , u T W. |
| Researcher Affiliation | Academia | 1RIKEN AIP, Japan 2National Key Laboratory for Novel Software Technology, Nanjing University, China 3The University of Tokyo, Japan. Correspondence to: Yu-Jie Zhang <EMAIL>, Peng Zhao <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Fixed-share For Continuous Space Algorithm 2 Follow-the-Leading-History Algorithm 3 Projected Fixed-share with Surrogate Loss |
| Open Source Code | No | A future direction is to develop computationally more efficient methods for the logistic loss and the general OCO setting. |
| Open Datasets | No | No specific datasets are mentioned for empirical evaluation. The paper is theoretical in nature and analyzes different loss functions (squared loss, logistic loss, etc.) without performing experiments on specific datasets. |
| Dataset Splits | No | No information about dataset splits is provided as the paper does not conduct empirical experiments with data. |
| Hardware Specification | No | No specific hardware details are mentioned as the paper is theoretical and does not report experimental results. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned, as the paper is theoretical and does not describe an implementation. |
| Experiment Setup | No | No specific experimental setup details, such as hyperparameter values or training configurations, are provided as the paper is theoretical. |