BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design

2019/09/10 by Shali Jiang, Jiang, Shali, Henry Chai +5 · 1 citation
Computer Science · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1909.04568

openalex publication_date 2019/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings. Computing the optimal policy for such problems requires solving Bellman equations, which are generally intractable. Most existing work resorts to severely myopic approximations by limiting the decision horizon to only a single time-step, which can underweight exploration in favor of exploitation. We present BINOCULARS: Batch-Informed NOnmyopic Choices, Using Long-horizons for Adaptive, Rapid SED, a general framework for deriving efficient, nonmyopic approximations to the optimal experimental policy. Our key idea is simple and surprisingly effective: we first compute a one-step optimal batch of experiments, then select a single point from this batch to evaluate. We realize BINOCULARS for Bayesian optimization and Bayesian quadrature -- two notable SED problems with radically different objectives -- and demonstrate that BINOCULARS significantly outperforms myopic alternatives in real-world scenarios.

Citations

• Practical Bayesian Optimization of Machine Learning Algorithms
• Parallel Predictive Entropy Search for Batch Global Optimization of\n Expensive Objective Functions
• Taking the Human Out of the Loop: A Review of Bayesian Optimization
• Batch Bayesian Optimization via Local Penalization
• Max-value Entropy Search for Efficient Bayesian Optimization
• Automated Model Selection with Bayesian Quadrature

Cited by

• Informing Acquisition Functions via Foundation Models for Molecular Discovery

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