youngadvance/efficient_nonmyopic_active_search

Code for three papers on active search published at ICML2017, NeurIPS2018, NeurIPS2019

Efficient Nonmyopic (batch) Active Search

This repo contains implentations of the active search policies in the following two papers:

[1]. Shali Jiang, Gustavo Malkomes, Geoff Converse, Alyssa Shofner, Benjamin Moseley, Roman Garnett. Efficient Nonmyopic Active Search. ICML 2017. http://proceedings.mlr.press/v70/jiang17d.html

[2]. Shali Jiang, Gustavo Malkomes, Matthew Abbott, Benjamin Moseley, Roman Garnett. Efficient Nonmyopic Batch Active Search. NeurIPS 2018. https://papers.nips.cc/paper/7387-efficient-nonmyopic-batch-active-search

[3]. Shali Jiang, Benjamin Moseley, Roman Garnett. Cost Effective Active Search. NeurIPS 2019. https://papers.nips.cc/paper/8734-cost-effective-active-search.pdf

Video

A 3-minute video introducing efficient nonmyopic batch active search: https://www.youtube.com/watch?v=9y1HNY95LzY&feature=youtu.be

How to use

Download the code, and checkout "demo.m" line 1-4 to see how to add dependencies, then run

>> demo

in Matlab to see how to use it.

Change parameter settings to try different datasets and policies. In particular, change which_setting to switch between budgeted or cost effective settings.

The code is partially tested on Ubuntu 18.04 with Matlab 2017b.

Dependencies

Active learning toolbox: https://github.com/rmgarnett/active_learning.git

Active search toolbox: https://github.com/rmgarnett/active_search.git

For drug discovery datasets: https://github.com/rmgarnett/active_virtual_screening.git

GPML package to generate toy problem: http://www.gaussianprocess.org/gpml/code/matlab/doc/