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 [1].
High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
Authors: Yuxuan Yin, Yu Wang, Peng Li
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed TSBO on various challenging high dimensional datasets and show superior data efficiency improvement. In a chemical design task (Sterling & Irwin, 2015) and an expression reconstruction task (Kusner et al., 2017), we achieve SOTA results compared to recent BO approaches. |
| Researcher Affiliation | Academia | Yuxuan Yin 1 Yu Wang 1 Peng Li 1 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, USA. |
| Pseudocode | Yes | Algorithm 1 Bi-Level Optimization of the Teacher-Student Model |
| Open Source Code | Yes | The implementation is available at https://github. com/reminiscenty/TSBO-Official. |
| Open Datasets | Yes | The first dataset comprises 40,000 single-variable arithmetic expressions, and is employed for an arithmetic expression reconstruction task (Kusner et al., 2017). The second ZINC250K dataset (Sterling & Irwin, 2015), consisting of 250,000 molecules, is used for two chemical design tasks with two objective molecule profiles: the penalized water-octanol partition coefficient (Penalized Log P) (G omez-Bombarelli et al., 2018) and the Ranolazine Multi Property Objective (Ranolazine MPO) (Brown et al., 2019). |
| Dataset Splits | Yes | Number of validation data 10 30 |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like MLP, GP with RBF kernel, and Adam optimizer, but does not provide specific version numbers for programming languages or libraries used in its implementation. |
| Experiment Setup | Yes | Table 6: Hyperparameters of TSBO |