On Approximate Thompson Sampling with Langevin Algorithms
Authors: Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conclude in Section F by validating these theoretical results in numerical simulations where we find that Thompson sampling with our approximate sampling schemes maintain the desirable performance of exact Thompson sampling. |
| Researcher Affiliation | Collaboration | 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA 2Google Research 3Halıcıo glu Data Science Institute, University of California, San Diego, USA 4Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA. |
| Pseudocode | Yes | Algorithm 1 Thompson sampling; Algorithm 2 (Stochastic Gradient) Langevin Algorithm for Arm a |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions "numerical simulations" but does not provide details about specific publicly available datasets, nor does it include links or citations for dataset access. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation methods). |
| Hardware Specification | No | The paper mentions running "numerical simulations" but does not specify any hardware details such as GPU/CPU models, memory, or cloud computing resources used. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | If we take the batch size k = O κ2 a , step size h(n) = O 1 n 1 κa La and number of steps N = O κ2 a in the SGLD algorithm |