Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Batched Thompson Sampling
Authors: Cem Kalkanli, Ayfer Ozgur
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Setup, 6 Experiments and Results, We evaluate BT-OFUL and BT-UCB against the batch Thompson Sampling (BTS) algorithm by considering both synthetic datasets and real-world datasets. |
| Researcher Affiliation | Academia | Yuehan Liu, Dongdong Ge, Weiran Shen Peking University |
| Pseudocode | Yes | Figure 1: Algorithm 1: BT-TS with fixed batch size, Figure 2: Algorithm 2: BT-OFUL, Figure 3: Algorithm 3: BT-UCB |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their methodology. |
| Open Datasets | Yes | We evaluate BT-OFUL and BT-UCB against the batch Thompson Sampling (BTS) algorithm by considering both synthetic datasets and real-world datasets. For real-world datasets, we use the Yahoo! Front Page Today Module (FP.Today) dataset [24] and MovieLens 1M dataset. |
| Dataset Splits | No | For the Yahoo! Today Front Page dataset, we use the first 200,000 samples for training and the next 100,000 samples for testing. No explicit mention of a 'validation' split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) are mentioned for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | For the synthetic datasets, we use a fixed learning rate of 0.01 for all algorithms. For the Yahoo! Today Front Page dataset, we set the batch size K = 1000, λ = 1, and η = 0.01. Similar parameters are used for MovieLens 1M dataset: batch size K=1000, λ = 1, η = 0.01. |