Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces

Kernkonzepte

Bounce proposes a reliable algorithm for optimizing high-dimensional black-box functions in combinatorial and mixed spaces, outperforming state-of-the-art methods.

Zusammenfassung

Leonard Papenmeier, Luigi Nardi, and Matthias Poloczek present the Bounce algorithm for high-dimensional Bayesian optimization.
The paper addresses the need for a reliable algorithm in optimizing high-dimensional black-box functions with mixed and combinatorial input spaces.
Existing methods have shown performance degradation when the unknown optima of the function lack a specific structure.
Bounce leverages nested embeddings to achieve reliable performance on various high-dimensional problems.
The algorithm uses trust region management and batch parallelism efficiently to optimize over continuous, combinatorial, and mixed spaces.

Statistiken

Bayesian optimization has numerous applications including hyperparameter optimization for machine learning models [9, 27].
Recent efforts have extended BO to combinatorial and mixed spaces [56].
Nested embeddings allow BO to handle a thousand input dimensions [60].

Zitate

"We propose Bounce (Bayesian optimization using increasingly high-dimensional combinatorial and continuous embeddings), a novel high-dimensional Bayesian optimization (HDBO) method that effectively optimizes over combinatorial, continuous, and mixed spaces."
"Bounce reliably achieves and often even improves upon state-of-the-art performance on a variety of high-dimensional problems."

Wichtige Erkenntnisse aus

Bounce

by Leonard Pape... um arxiv.org 03-21-2024

https://arxiv.org/pdf/2307.00618.pdf

Tiefere Fragen

How can Bounce's approach to nested embeddings be applied in other optimization domains

Bounce's approach to nested embeddings can be applied in various optimization domains beyond combinatorial spaces. For instance, in hyperparameter tuning for machine learning models, where the search space consists of a mix of continuous and categorical variables, Bounce's method of partitioning input variables into bins based on their types can be beneficial. By leveraging nested embeddings with increasing dimensionality, the algorithm can efficiently optimize over mixed spaces while maintaining scalability and reliability. This approach could also be valuable in optimizing complex engineering designs that involve a combination of discrete and continuous parameters.

What are the implications of BODi's sensitivity to the location of optima on real-world applications

BODi's sensitivity to the location of optima has significant implications for real-world applications, especially those involving combinatorial optimization problems. The algorithm's performance degradation when the optimum is moved to a random location or when categories are shuffled highlights its reliance on specific structures within the optimizer. In practical scenarios such as materials discovery or chemical engineering, where optimal solutions may not exhibit predictable patterns or structures, BODi's sensitivity could lead to suboptimal outcomes and hinder progress. This limitation underscores the importance of developing robust optimization algorithms that can handle diverse problem landscapes without being overly sensitive to specific configurations.

How can the reliability of Bayesian optimization algorithms impact advancements in materials discovery

The reliability of Bayesian optimization algorithms like Bounce plays a crucial role in advancing materials discovery by enabling efficient exploration of high-dimensional parameter spaces with mixed variable types. In this context, reliable optimization methods are essential for accelerating research processes such as identifying novel materials with desired properties or optimizing chemical reactions for improved efficiency. By providing consistent and accurate results across various problem settings, reliable Bayesian optimization algorithms contribute to streamlining experimentation processes in materials science and facilitating the discovery of new compounds or formulations with enhanced characteristics. Additionally, these advancements have broader implications for industries reliant on material innovation, including pharmaceuticals, electronics manufacturing, and renewable energy sectors.