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..
Generalizing Bayesian Optimization with Decision-theoretic Entropies
Authors: Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed method on the example tasks described in Section 5: top-k optimization with diversity, multi-level set estimation, and sequence search. For these applications, we show comparisons against a set of baselines on real and synthetic black-box functions. |
| Researcher Affiliation | Academia | Computer Science Department, Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | Yes | Algorithm 1 H ,A-ENTROPY SEARCH |
| Open Source Code | Yes | All code and instructions are included in supplementary material. |
| Open Datasets | Yes | We also compare each method on the Vaccination function (provided by [53]), which returns the vaccination rate for locations in the continential United States, given an input (latitude, longitude). [...] In the top row, we compare the performance of all methods, showing the accuracy vs. iteration. Here, the Pennsylvania Night Light function [1] released by NASA (additional details in the appendix), returns the relative level of light at a location in Pennsylvania, as queried by a satellite image. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See appendix B. |
| Software Dependencies | No | The paper mentions 'GPy Torch [17] and Bo Torch [4]' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | No | The paper states 'All training details are specified in the paper and included code,' but does not provide specific hyperparameter values, training configurations, or system-level settings in the main text. |