Adaptive Sampling for Discovery
Authors: Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The benefits of IDS are shown in both simulation experiments and real-data experiments for discovering chemical reaction conditions. We start with simulation studies on the three problems: (generalized) linear model, low-rank matrix and graph model. |
| Researcher Affiliation | Academia | Ziping Xu Department of Statistics University of Michigan zipingxu@umich.edu Eunjae Shim Department of Chemistry University of Michigan eunjae@umich.edu Ambuj Tewari Department of Statistics University of Michigan tewaria@umich.edu Paul Zimmerman Department of Chemistry University of Michigan paulzim@umich.edu |
| Pseudocode | Yes | Algorithm 1 IDS (Information-Directed Sampling) for Discovery |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] The codes and dataset are in the supplemental material. |
| Open Datasets | Yes | The first dataset, named Photoredox Nickel Dual-Catalysis (PNDC) [11], comes from a reaction that involves two types of catalysts... The second C-N Cross-Coupling with Isoxazoles (CNCCI) dataset [2] comes from a collection of a cross-coupling reactions... |
| Dataset Splits | No | The paper states 'They [training details, e.g. data splits] are provided along with code' in the checklist, but the main text does not explicitly detail the training/validation/test dataset splits, percentages, or specific cross-validation setup. |
| Hardware Specification | No | The checklist states 'Did you include the total amount of compute and the type of resources used...? [Yes] They are given in Appendix H'. However, Appendix H within the provided paper content does not contain specific hardware details such as GPU/CPU models or cloud provider specifications. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., library names with versions) in its main text or appendices. |
| Experiment Setup | Yes | The paper mentions specific experimental parameters for simulation studies: 'We study the effects of d = 20, 50, 100' and 'We tested σ2 ϵ = 0.1, 1.0, 10'. For real-data experiments, it states, 'We tuned hyperparameters for all three algorithms. Details are given in Appendix H.' |