Sample-Then-Optimize Batch Neural Thompson Sampling
Authors: Zhongxiang Dai, YAO SHU, Bryan Kian Hsiang Low, Patrick Jaillet
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 speciļ¬ed otherwise. We use L = 2, m = 256 for all methods. |