Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Authors: Fengxue Zhang, Jialin Song, James C Bowden, Alexander Ladd, Yisong Yue, Thomas Desautels, Yuxin Chen
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically the effectiveness of BALLET on both synthetic and real-world optimization tasks. and 4. Experiment |
| Researcher Affiliation | Collaboration | Fengxue Zhang 1 Jialin Song 2 James Bowden 3 Alexander Ladd 4 Yisong Yue 3 Thomas A. Desautels 4 Yuxin Chen 1 1Departmet of Computer Science, University of Chicago, Illinois, U.S. 2Nvidia, California, U.S. 3California Institute of Technology, California, U.S. 4Lawrence Livermore National Laboratory, California, U.S.. |
| Pseudocode | Yes | Algorithm 1 Bayesian Optimization with Adaptive Level-Set Estimation (BALLET) |
| Open Source Code | No | The paper refers to open-sourced implementations of baseline algorithms (LA-MCTS and Tu RBO) but does not provide a statement or link for its own proposed method (BALLET). |
| Open Datasets | Yes | Water Converter Configuration-32D. This UCI dataset we use consists of positions and absorbed power outputs of wave energy converters (WECs) from the southern coast of Sydney. and Nanophotonics Structure Design-5D. We wish to optimize a weighted figure of merit quantifying the fitness of the transmission spectrum for hyperspectral imaging as assessed by a numerical solver (Song et al., 2018). and GB1-118D. ... (Wu et al., 2019). and Rosetta Protein Design-86D. ... (Desautels et al., 2020; 2022). |
| Dataset Splits | No | The paper mentions 'warm-up' sets for initial observations but does not provide specific details on training, validation, and test dataset splits with percentages or counts for model evaluation. |
| Hardware Specification | No | The paper does not specify the exact hardware components such as GPU or CPU models, or details of cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper mentions various software components and models like Deep Kernel Learning, KISS-GP, and Auto-Encoder, but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | The neural network consists of three hidden layers with 1000, 500, and 50 neurons, and Re LU non-linearity respectively. The output layer is one-dimensional. We use squared exponential kernel or linear kernel as the base kernel... and Thompson Sampling (Chapelle & Li, 2011) for the acquisition function α. For each of the algorithms, the same 10 randomly picked points serve as the warm-up set. For BALLET-ICI, we set δ in Lemma 1 to be 0.2. Through the experiments, we fix β1/2 t = 0.2 only when identifying ROIs as in line 4 of Algorithm 1. |