Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved Regret for Bandit Convex Optimization with Delayed Feedback
Authors: Yuanyu Wan, Chang Yao, Mingli Song, Lijun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,895 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,981 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |