Bayesian Algorithm Execution (BAX)

Code for the paper:

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
International Conference on Machine Learning (ICML), 2021
arXiv:2104.09460

See also the BAX website for additional details and animations.

One-sentence summary

Extending Bayesian optimization from estimating global optima to estimating other function properties defined by the output of algorithms.

Abstract

In many real world problems, we want to infer some property of an expensive black-box function f, given a budget of T function evaluations. One example is budget constrained global optimization of f, for which Bayesian optimization is a popular method. Other properties of interest include local optima, level sets, integrals, or graph-structured information induced by f. Often, we can find an algorithm A to compute the desired property, but it may require far more than T queries to execute. Given such an A, and a prior distribution over f, we refer to the problem of inferring the output of A using T evaluations as Bayesian Algorithm Execution (BAX).

To tackle this problem, we present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output. Applying this to Dtra's algorithm, for instance, we infer shortest paths in synthetic and real-world graphs with black-box edge costs. Using evolution strategies, we yield variants of Bayesian optimization that target local, rather than global, optima. On these problems, InfoBAX uses up to 500 times fewer queries to f than required by the original algorithm. Our method is closely connected to other Bayesian optimal experimental design procedures such as entropy search methods and optimal sensor placement using Gaussian processes.

Installation

This repo requires Python 3.6+. To install Python dependencies, cd into this repo and run:

$ pip install -r requirements/requirements.txt
$ pip install -r requirements/requirements_gpfs.txt

Note that this installs dependencies for GPflowSampling, which our implementation uses to efficiently run algorithms on GP posterior function samples.

For some functionality, you'll also need to compile a Stan model by running:

$ python bax/models/stan/compile_models.py

Examples

[WIP] More examples are in the process of being merged into this branch. Note that the following API and functionality may undergo changes, as this library is still in the early stages.

First make sure this repo directory is on the PYTHONPATH, e.g. by running:

$ source shell/add_pwd_to_pythonpath.sh

Example 1: Estimating shortest paths in graphs

For a demo on a 10x10 grid graph, run:

$ python examples/dijkstra/bax_grid10_viz_simple_demo.py

To produce images for an animation on a 20x20 grid graph, run:

$ python examples/dijkstra/bax_grid20_animation.py

Example 2: Bayesian local optimization

For a demo on a two-dimensional optimization problem, run:

$ python examples/branin/bax_viz2d_simple_demo.py

 

Example 3: Top-k estimation

For a demo on a top-10 estimation task over a discrete set of points, run:

$ python examples/topk/bax_simple_demo.py

Citation

Please cite our paper if you find this project helpful:

@inproceedings{neiswanger2021bayesian,
  title         = {Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information},
  author        = {Neiswanger, Willie and Wang, Ke Alexander and Ermon, Stefano},
  booktitle     = {International Conference on Machine Learning},
  year          = {2021},
  organization  = {PMLR}
}