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..
Sample-Then-Optimize Batch Neural Thompson Sampling
Authors: Zhongxiang Dai, YAO SHU, Bryan Kian Hsiang Low, Patrick Jaillet
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we verify their empirical effectiveness using practical Auto ML and reinforcement learning experiments. We demonstrate our empirical effectiveness in real-world experiments including automated machine learning (Auto ML) and reinforcement learning (RL) tasks, as well as a task on optimization over images. |
| Researcher Affiliation | Academia | Dept. of Computer Science, National University of Singapore, Republic of Singapore Dept. of Electrical Engineering and Computer Science, MIT, USA |
| Pseudocode | Yes | Algorithm 1 STO-BNTS Algorithm 2 STO-BNTS-Linear |
| Open Source Code | Yes | Our code is available at https://github.com/daizhongxiang/sto-bnts. |
| Open Datasets | Yes | For the RF and XGBoost hyperparameter tuning tasks, we use the datasets for diabetes diagnosis and MNIST, respectively, from OpenML [63, 64]. For the CNN hyperparameter tuning tasks, we use the MNIST dataset. All these datasets are publicly available from OpenML. |
| Dataset Splits | Yes | We use a random 80/10/10 train/validation/test split for the diabetes diagnosis and MNIST datasets. |
| Hardware Specification | Yes | All experiments are run on a cluster with NVIDIA A100 GPUs. |
| Software Dependencies | No | All algorithms are implemented in Python and PyTorch. (No version numbers provided) |
| Experiment Setup | Yes | For all methods, we use an NN architecture with a depth of L = 8 and a width of m = 64 unless specified otherwise. We use L = 2, m = 256 for all methods. |