Projection-free Online Learning over Strongly Convex Sets
Authors: Yuanyu Wan, Lijun Zhang10076-10084
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the special case of online learning over strongly convex sets, for which we first prove that OFW can enjoy a better regret bound of O(T 2/3) for general convex losses. ... Theoretical analysis reveals that SC-OFW for strongly convex OCO attains a regret bound of O(T 2/3) over general convex sets1 and a better regret bound of O( T) over strongly convex sets. |
| Researcher Affiliation | Academia | Yuanyu Wan1, Lijun Zhang1,2, 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2Pazhou Lab, Guangzhou 510330, China {wanyy, zhanglj}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1: OFW with Line Search |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not provide hardware specifications for experimental runs. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup or hyperparameters. |