Improved Regret for Bandit Convex Optimization with Delayed Feedback
Authors: Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare our D-FTBL against GOLD [Héliou et al., 2020] and improved GOLD [Bistritz et al., 2022] by conducting simulation experiments on two publicly available data sets ijcnn1 and SUSY from the LIBSVM repository [Chang and Lin, 2011]. All algorithms are implemented with Python, and tested on a laptop with 2.4GHz CPU and 16GB memory. |
| Researcher Affiliation | Academia | 1School of Software Technology, Zhejiang University, Ningbo, China 2State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China 3Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China 4National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China |
| Pseudocode | Yes | Algorithm 1 Delayed Follow-The-Bandit-Leader |
| Open Source Code | No | Any interested people can send the authors an email to query the source code. |
| Open Datasets | Yes | We compare our D-FTBL against GOLD [Héliou et al., 2020] and improved GOLD [Bistritz et al., 2022] by conducting simulation experiments on two publicly available data sets ijcnn1 and SUSY from the LIBSVM repository [Chang and Lin, 2011]. |
| Dataset Splits | No | For all algorithms, c and c are respectively selected from {0.1, 1.0, 10} and {0.01, 0.1, . . . , 100} simply according to their performance for d = 200. |
| Hardware Specification | Yes | All algorithms are implemented with Python, and tested on a laptop with 2.4GHz CPU and 16GB memory. |
| Software Dependencies | No | All algorithms are implemented with Python, and tested on a laptop with 2.4GHz CPU and 16GB memory. |
| Experiment Setup | Yes | According to the previous discussions about Theorem 1, we set α = 0, K = n T , δ = c n T 1/4, and η = c / max{ Td, n T 3/4} for our D-FTBL by tuning these two constants c and c . For those two baselines, we only need to set parameters δ and η. In addition to the theoretically suggested value of δ and η, we also introduce c and c as the scale factor, respectively. For all algorithms, c and c are respectively selected from {0.1, 1.0, 10} and {0.01, 0.1, . . . , 100} simply according to their performance for d = 200. Moreover, due to the randomness of these algorithms, we repeat them 20 times and report the average of their total loss. |