PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Authors: Bianca Dumitrascu, Karen Feng, Barbara Engelhardt
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 offline 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 biancad@princeton.edu Karen Feng Department of Computer Science Princeton University Princeton, NJ 08540 karenfeng@princeton.edu Barbara E Engelhardt Department of Computer Science Princeton University Princeton, NJ 08540 bee@princeton.edu |
| 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. |