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 [1].
Online Convex Optimization with Continuous Switching Constraint
Authors: Guanghui Wang, Yuanyu Wan, Tianbao Yang, Lijun Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We ο¬rst investigate the hardness of the problem, and provide a lower bound... We then develop a simple gradient-based algorithm which enjoys the minimax optimal regret bound. Finally, we show that, for strongly convex functions, the regret bound can be improved... In this section, we present the algorithms and theoretical guarantees for OCO-CSC. |
| Researcher Affiliation | Academia | Guanghui Wang1, Yuanyu Wan1,2, Tianbao Yang3, Lijun Zhang1,2, 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Peng Cheng Laboratory, Shenzhen, Guangdong, China 3Department of Computer Science, The University of Iowa, Iowa City, USA |
| Pseudocode | Yes | Algorithm 1 Adversary s Policy |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets or specify any data splitting for validation. |
| Hardware Specification | No | As a theoretical paper, it does not describe experimental setup including hardware specifications. |
| Software Dependencies | No | As a theoretical paper, it does not describe software dependencies with version numbers. |
| Experiment Setup | No | As a theoretical paper, it does not describe an experimental setup with specific hyperparameters or training settings. |