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..
Langevin Monte Carlo for Contextual Bandits
Authors: Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on both synthetic data and real-world datasets on different contextual bandit models, which demonstrates that directly sampling from the posterior is both computationally efficient and competitive in performance. |
| Researcher Affiliation | Academia | 1Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA 2Department of Computer Science, Purdue University, West Lafayette, IN, USA. |
| Pseudocode | Yes | Algorithm 1 Langevin Monte Carlo Thompson Sampling (LMC-TS) |
| Open Source Code | Yes | Our implementation can be found at https://github.com/devzhk/LMCTS. |
| Open Datasets | Yes | We conduct experiments on both synthetic datasets and real-world datasets (UCI machine learning datasets and a high dimensional image dataset CIFAR10)... |
| Dataset Splits | No | The paper mentions using synthetic datasets, UCI machine learning datasets, and CIFAR10. It describes how context vectors are constructed for classification datasets but does not provide specific train/validation/test split percentages or counts, nor does it refer to predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | All experiments are conducted on Amazon EC2 P3 instances with NVIDIA V100 GPUs and Broadwell E5-2686 v4 processors. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, specific library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | For LMC-TS, we set the step size ηt = η0/t as suggested in our theory and do a grid search for the constant η0 and the temperature parameter β-1. We fix the epoch length for the inner loop of our algorithm as Kt = 100 for all t... Neural networks are all updated by 100 gradient descent steps every round. |