Pareto-frontier Entropy Search with Variational Lower Bound Maximization
This study considers multi-objective Bayesian optimization (MOBO) through the information gain of the Pareto-frontier. To calculate the information gain, a predictive distribution conditioned on the Pareto-frontier plays a key role, which is defined as a distribution truncated by the Pareto-frontier. However, it is usually impossible to obtain the entire Pareto-frontier in a continuous domain, and therefore, the complete truncation cannot be known. We consider an approximation of the truncate distribution by using a mixture distribution consisting of two possible approximate truncation obtainable from a subset of the Pareto-frontier, which we call over- and under-truncation. Since the optimal balance of the mixture is unknown beforehand, we propose optimizing the balancing coefficient through the variational lower bound maximization framework, by which the approximation error of the information gain can be minimized. Our empirical evaluation demonstrates the effectiveness of the proposed method particularly when the number of objective functions is large.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationSimilar Papers 제목 키워드 기반
$\{\text{PF}\}^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
We present Parallel Feasible Pareto Frontier Entropy Search ($\{\text{PF}\}^2$ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch q…
Bayesian OptimizationMulti-objective Bayesian Optimization using Pareto-frontier Entropy
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among…
Bayesian OptimizationFairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems
Designing fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals: More fairness might require sacrificing some performance and vice versa, yet the space of possib…
Decision MakingApproximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning
Many real-word decision or control problems involve multiple conflicting objectives and uncertainties, which requires learned policies are not only Pareto optimal but also robust. In this paper, we proposed a novel algor…
Bayesian OptimizationDiversityMulti-Objective Reinforcement Learningreinforcement-learning+2Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints
We present MESMOC+, an improved version of Max-value Entropy search for Multi-Objective Bayesian optimization with Constraints (MESMOC). MESMOC+ can be used to solve constrained multi-objective problems when the objectiv…
Bayesian Optimization