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 Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
Authors: Ahmadreza Moradipari, Mohammad Pedramfar, Modjtaba Shokrian Zini, Vaneet Aggarwal
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,798 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,717 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 | In this paper, we prove the first Bayesian regret bounds for Thompson Sampling in reinforcement learning in a multitude of settings. |
| Researcher Affiliation | Collaboration | Toyota Motor North America, Info Tech Labs, Mountain View, CA, USA, EMAIL Purdue University, West Lafayette, IN, USA, EMAIL EMAIL Purdue University, West Lafayette, IN, USA, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that code is being released or made available. |
| Open Datasets | No | The paper does not provide concrete access information for a publicly available or open dataset, as it is a theoretical paper and does not report on experiments. |
| 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 data partitioning, as it is a theoretical paper and does not report on experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running experiments, as it is a theoretical paper and does not report on experimental setups. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate experiments, as it is a theoretical paper. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text, as it is a theoretical paper and does not describe experiments. |