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].
Optimizing Dynamic Structures with Bayesian Generative Search
Authors: Minh Hoang, Carleton Kingsford
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section evaluates and reports the empirical performance of our kernel selection framework DTERGENS on a synthetic kernel recovery task and kernel selection for regression on three real-world datasets. |
| Researcher Affiliation | Academia | 1Computer Science Department, School of Computer Science, Carnegie Mellon University, USA 2Computational Biology Department, School of Computer Science, Carnegie Mellon University, USA. |
| Pseudocode | Yes | Algorithm 1 DTERGENS Kernel Selection |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code specific to the methodology described. |
| Open Datasets | Yes | The DIABETES dataset (Efron et al., 2004) containing 442 diabetes patient records... The MAUNA LOA (Mauna Loa Atmospheric Carbon Dioxide) dataset (Keeling & Whorf, 2004)... The PROTEIN dataset (Rana, 2013) featuring 45730 observations of protein tertiary structures... |
| Dataset Splits | Yes | 80-10-10 train-test-validation split (i.e., we use the validation fold to compute BO feedback and the test fold to evaluate final performance); |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies (e.g., libraries, frameworks, or languages). |
| Experiment Setup | Yes | For all experiments, we demonstrate the performance of our framework on the black-box model Variational DTC Sparse Gaussian Process (v DTC) (Hensman et al., 2013) with the following configurations: (1) 80-10-10 train-test-validation split (i.e., we use the validation fold to compute BO feedback and the test fold to evaluate final performance); (2) 100 randomly selected inducing inputs; (3) kernel hyperparameters are optimized using L-BFGS over 100 iterations. |