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..
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Authors: Bianca Dumitrascu, Karen Feng, Barbara Engelhardt
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate and compare our PG-TS method with Laplace-TS. We evaluate our algorithm in three scenarios: simulated data sets with parameters sampled from Gaussian and mixed Gaussian distributions, a toy data set based on the Forest Cover Type data set from the UCI repository, and an ofο¬ine evaluation method for bandit algorithms that relies on real-world log data. |
| Researcher Affiliation | Academia | Bianca Dumitrascu Lewis Sigler Institute for Integrative Genomics Princeton University Princeton, NJ 08540 EMAIL Karen Feng Department of Computer Science Princeton University Princeton, NJ 08540 EMAIL Barbara E Engelhardt Department of Computer Science Princeton University Princeton, NJ 08540 EMAIL |
| Pseudocode | Yes | Algorithm 1 PG-TS |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | We further compared these methods using the Forest Cover Type data from the UCI Machine Learning repository [8]. |
| Dataset Splits | No | The paper describes the sequential processing of data for online learning in a bandit setting and mentions the number of trials or events used, but it does not specify traditional train/validation/test dataset splits as commonly found in supervised learning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific solvers). |
| Experiment Setup | Yes | We sample from the PG distribution [24, 27] including M = 100 burn-in steps. This number is empirically tuned... We set the hyperparameters b = 0, and B = I10. |