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..
Adaptive Online Learning in Dynamic Environments
Authors: Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study online convex optimization in dynamic environments, and aim to bound the dynamic regret with respect to any sequence of comparators. ... we develop a novel online method, namely adaptive learning for dynamic environment (Ader), which achieves an optimal O( p T(1 + PT )) dynamic regret. The basic idea is to maintain a set of experts, each attaining an optimal dynamic regret for a specific path-length, and combines them with an expert-tracking algorithm. ... We establish the first lower bound for the general regret bound in (2), which is Ω( p T(1 + PT )). We develop a serial of novel methods for minimizing the general dynamic regret, and prove an optimal O( p T(1 + PT )) upper bound. |
| Researcher Affiliation | Academia | Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Ader: Meta-algorithm; Algorithm 2 Ader: Expert-algorithm; Algorithm 3 Improved Ader: Meta-algorithm; Algorithm 4 Improved Ader: Expert-algorithm; Algorithm 5 Ader: Expert-algorithm with dynamical models |
| Open Source Code | No | The paper does not provide any specific statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper does not describe any experiments involving datasets, training, or data access information. |
| Dataset Splits | No | The paper does not describe any experimental setup involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not mention any specific hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper does not detail any experimental setup, hyperparameters, or system-level training settings as it focuses on theoretical development. |