Nonmyopic Multifidelity Acitve Search
Authors: Quan Nguyen, Arghavan Modiri, Roman Garnett
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our solution on real-world datasets and demonstrate significantly better performance than natural benchmarks. |
| Researcher Affiliation | Academia | 1Washington University in St. Louis, MO, USA 2University of Toronto, Toronto, Canada. |
| Pseudocode | Yes | We give the pseudocode for the policy in the appendix. |
| Open Source Code | Yes | Matlab implementations of our policies are available at: https://github.com/KrisNguyen135/multifidelity-active-search . |
| Open Datasets | Yes | Here we used the first 50 proteins from the Binding DB database (Liu et al., 2007) described by Jiang et al. (2017). A set of 100 000 compounds sampled from the ZINC database (Sterling & Irwin, 2015) served as a shared negative set. ...This dataset comprises 106 810 alloys from the materials literature (Kawazoe et al., 1997; Ward et al., 2016) |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with percentages or sample counts. It describes an iterative active search process within a budget. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states 'Matlab implementations of our policies', but does not provide specific version numbers for Matlab or any other software dependencies. |
| Experiment Setup | Yes | We set θ {0.1, 0.3}. We set k, the number of L queries that are made for each H query, to be either 2 or 5, and set the budget on H to be 300. ... In our experiments, we set u = s = 500 for MF ENS. ... We set β = 0.01 for L queries and β = 0.001 for H queries, as suggested in the same work. |