Generalizing Bayesian Optimization with Decision-theoretic Entropies
Authors: Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {neiswanger,lantaoyu,sjzhao,chenlin,ermon}@cs.stanford.edu |
| 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 speciļ¬ed in the paper and included code,' but does not provide specific hyperparameter values, training configurations, or system-level settings in the main text. |