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.
Scalar Posterior Sampling with Applications
Authors: Georgios Theocharous, Zheng Wen, Yasin Abbasi Yadkori, Nikos Vlassis
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 13,846 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,593 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 through simulations the performance of DS-PSRL algorithm with the latest PSRL algorithm called Thompson Sampling with dynamic episodes (TSDE) Ouyang et al. [2017b]. We experimented with the River Swim environment Strehl and Littman [2008], which was the domain used to show how TSDE outperforms all known existing algorithms in Ouyang et al. [2017b]. |
| Researcher Affiliation | Industry | Georgios Theocharous Adobe Research EMAIL Zheng Wen Adobe Research EMAIL Yasin Abbasi-Yadkori Adobe Research EMAIL Nikos Vlassis Netflix EMAIL |
| Pseudocode | Yes | Figure 1: The DS-PSRL algorithm with deterministic schedule of policy updates. Inputs: P1, the prior distribution of . L 1. for t 1, 2, . . . do if t = L then Sample e t Pt. L 2L. else e t e t 1. end if Calculate near-optimal action at (xt, e t). Execute action at and observe the new state xt+1. Update Pt with (xt, at, xt+1) to obtain Pt+1. end for |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We experimented with the River Swim environment Strehl and Littman [2008], which was the domain used to show how TSDE outperforms all known existing algorithms in Ouyang et al. [2017b]. |
| Dataset Splits | No | The paper mentions using the River Swim environment and various settings for experiments but does not explicitly specify training, validation, and test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers that are required to reproduce the experiments. |
| Experiment Setup | Yes | The MDP consists of K states arranged in a chain with the agent starting in the leftmost state (s = 1). The reward function is given by: r(s, a) = 5 if s = 1, a = left; r(s, a) = 10000 if s = K, a = right; and r(s, a) = 0 otherwise. We assumed the true model of the world was = 2 and that the agent starts in the left-most state. The initial parameters of the priors were set to one (uniform) for the non-zero transition probabilities of the River Swim problem and zero otherwise. In our experiment we set n = 2 and d = 2. |