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Bayesian Optimization over Hybrid Spaces

We consider the problem of optimizing hybrid structures (mixture of discrete and continuous input variables) via expensive black-box function evaluations. This problem arises in many real-world applications. For example, in materials design …

High-Throughput Training of Deep CNNs on ReRAM-based Heterogeneous Architectures via Optimized Normalization Layers

Resistive random-access memory (ReRAM)-based architectures can be used to accelerate Convolutional Neural Network (CNN) training. However, existing architectures either do not support normalization at all or they support only a limited version of it. …

Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs - An Information-Theoretic Approach

Mobile system-on-chips (SoCs) are growing in their complexity and heterogeneity (e.g., Arm Big-Little architecture) to meet the needs of emerging applications, including games and artificial intelligence. This makes it very challenging to optimally …

Mercer Features for Efficient Combinatorial Bayesian Optimization

Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations. This paper addresses the BO problem setting for combinatorial spaces (e.g., sequences and graphs) that occurs …

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Many real-world applications involve black-box optimization of multiple objectives using continuous function approximations that trade-off accuracy and resource cost of evaluation. For example, in rocket launching research, we need to find designs …

Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints

We consider the problem of constrained multi-objective blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of …

Scalable Combinatorial Bayesian Optimization with Tractable Statistical models

We study the problem of optimizing expensive blackbox functions over combinatorial spaces (eg, sets, sequences, trees, and graphs). BOCS (Baptista and Poloczek, 2018) is a state-of-the-art Bayesian optimization method for tractable statistical …