143 citations · 381 across the 13 of their papers we have counts for
15 papers
Understanding High-Dimensional Bayesian Optimization
Leonard Papenmeier, Matthias Poloczek, Luigi Nardi
Recent work reported that simple Bayesian optimization (BO) methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. Thi…
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions w…
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applicati…
Scalable Constrained Bayesian Optimization
David Eriksson, Matthias Poloczek
The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering. These problems are c…
Bayesian Optimization Allowing for Common Random Numbers
Michael Pearce, Matthias Poloczek, Juergen Branke
Bayesian optimization is a powerful tool for expensive stochastic black-box optimization problems such as simulation-based optimization or machine learning hyperparameter tuning. M…
Dynamic Subgoal-based Exploration via Bayesian Optimization
Yijia Wang, Matthias Poloczek, Daniel R. Jiang
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by comp…
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob R Gardner +2
Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional…
Bayesian Optimization of Combinatorial Structures
Ricardo Baptista, Matthias Poloczek
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The com…
Comparing the Finite-Time Performance of Simulation-Optimization Algorithms
Naijia Dong, David J. Eckman, Matthias Poloczek +2
We empirically evaluate the finite-time performance of several simulation-optimization algorithms on a testbed of problems with the goal of motivating further development of algori…
Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson +1
Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimi…
Warm Starting Bayesian Optimization
Matthias Poloczek, Jialei Wang, Peter I. Frazier
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related proble…
Multi-Information Source Optimization
Matthias Poloczek, Jialei Wang, Peter I. Frazier
We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective. In general, such a…
Greedy Matching: Guarantees and Limitations
Bert Besser, Matthias Poloczek
Since Tinhofer proposed the MinGreedy algorithm for maximum cardinality matching in 1984, several experimental studies found the randomized algorithm to perform excellently for var…
Contagious Sets in Dense Graphs
Daniel Freund, Matthias Poloczek, Daniel Reichman
We study the activation process in undirected graphs known as bootstrap percolation: a vertex is active either if it belongs to a set of initially activated vertices or if at some…
On Some Recent MAX SAT Approximation Algorithms
Matthias Poloczek, David P. Williamson, Anke van Zuylen
Recently a number of randomized 3/4-approximation algorithms for MAX SAT have been proposed that all work in the same way: given a fixed ordering of the variables, the algorithm ma…