Adaptive Online Learning in Dynamic Environments
Authors: Lijun Zhang, Shiyin Lu, Zhi-Hua Zhou
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 {zhanglj, lusy, zhouzh}@lamda.nju.edu.cn |
| 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. |