318 research outputs found
Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature
Real-world optimisation problems often feature complex combinations of (1)
diverse constraints, (2) discrete and mixed spaces, and are (3) highly
parallelisable. (4) There are also cases where the objective function cannot be
queried if unknown constraints are not satisfied, e.g. in drug discovery,
safety on animal experiments (unknown constraints) must be established before
human clinical trials (querying objective function) may proceed. However, most
existing works target each of the above three problems in isolation and do not
consider (4) unknown constraints with query rejection. For problems with
diverse constraints and/or unconventional input spaces, it is difficult to
apply these techniques as they are often mutually incompatible. We propose
cSOBER, a domain-agnostic prudent parallel active sampler for Bayesian
optimisation, based on SOBER of Adachi et al. (2023). We consider infeasibility
under unknown constraints as a type of integration error that we can estimate.
We propose a theoretically-driven approach that propagates such error as a
tolerance in the quadrature precision that automatically balances exploitation
and exploration with the expected rejection rate. Moreover, our method flexibly
accommodates diverse constraints and/or discrete and mixed spaces via adaptive
tolerance, including conventional zero-risk cases. We show that cSOBER
outperforms competitive baselines on diverse real-world blackbox-constrained
problems, including safety-constrained drug discovery, and
human-relationship-aware team optimisation over graph-structured space.Comment: 24 pages, 5 figure
A portfolio approach to massively parallel Bayesian optimization
One way to reduce the time of conducting optimization studies is to evaluate
designs in parallel rather than just one-at-a-time. For expensive-to-evaluate
black-boxes, batch versions of Bayesian optimization have been proposed. They
work by building a surrogate model of the black-box that can be used to select
the designs to evaluate efficiently via an infill criterion. Still, with higher
levels of parallelization becoming available, the strategies that work for a
few tens of parallel evaluations become limiting, in particular due to the
complexity of selecting more evaluations. It is even more crucial when the
black-box is noisy, necessitating more evaluations as well as repeating
experiments. Here we propose a scalable strategy that can keep up with massive
batching natively, focused on the exploration/exploitation trade-off and a
portfolio allocation. We compare the approach with related methods on
deterministic and noisy functions, for mono and multiobjective optimization
tasks. These experiments show similar or better performance than existing
methods, while being orders of magnitude faster
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