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..
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 |