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.
Simultaneously Learning Stochastic and Adversarial Bandits under the Position-Based Model
Authors: Cheng Chen, Canzhe Zhao, Shuai Li6202-6210
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,247 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,764 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 | The experiments show that our algorithm could simultaneously learn in both stochastic and adversarial environments and is competitive compared to existing methods that are designed for a single environment. |
| Researcher Affiliation | Academia | 1 Nanyang Technological University 2 Shanghai Jiao Tong University |
| Pseudocode | Yes | Algorithm 1: FTRL-PBM |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes synthetic data generation parameters and mentions real-world data deferred to Appendix, but does not provide concrete access information (link, DOI, repository, or formal citation) for any publicly available or open dataset used for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For all experiments, we use n = 10 items and m = 5 positions. For the synthetic data, we set the position examination probabilities to β = (1, 1/5) which are commonly adopted in previous works (Wang et al. 2018; Li, Lattimore, and Szepesv ari 2019). The attractiveness of items are set as α = (0.95, 0.95^2 , ..., 0.95^9 ). We consider two cases of Δ = 0.03 and Δ = 0.01. |