Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimizing Dynamic Structures with Bayesian Generative Search
Authors: Minh Hoang, Carleton Kingsford
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,239 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,007 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |