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.
Bounded Memory Adversarial Bandits with Composite Anonymous Delayed Feedback
Authors: Zongqi Wan, Xiaoming Sun, Jialin Zhang
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,708 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,340 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show non-oblivious setting incurs Ω(T) pseudo regret even when the loss sequence is bounded memory. However, we propose a wrapper algorithm which enjoys o(T) policy regret on many adversarial bandit problems with the assumption that the loss sequence is bounded memory. Especially, for K-armed bandit and bandit convex optimization, we have O(T 2/3) policy regret bound. We also prove a matching lower bound for K-armed bandit. |
| Researcher Affiliation | Academia | 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Mini-batch wrapper |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on proving bounds and developing algorithms, not on empirical training with datasets. No dataset information is provided for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with datasets. No validation dataset splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |