113 citations · 294 across the 24 of their papers we have counts for
11 papers · 1 filter
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…
PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning
Neeratyoy Mallik, Edward Bergman, Carl Hvarfner +5
Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been develo…
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…
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Carl Hvarfner, Erik Hellsten, Frank Hutter +1
Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full poten…
Joint Entropy Search for Maximally-Informed Bayesian Optimization
Carl Hvarfner, Frank Hutter, Luigi Nardi
Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Searc…
BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Carl Hvarfner, Danny Stoll, Artur Souza +3
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sampl…
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon +1
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is…
Bayesian Optimization with a Prior for the Optimum
Artur Souza, Luigi Nardi, Leonardo B. Oliveira +3
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to was…
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
Artur Souza, Leonardo B. Oliveira, Sabine Hollatz +5
Serial crystallography is the field of science that studies the structure and properties of crystals via diffraction patterns. In this paper, we introduce a new serial crystallogra…
Practical Design Space Exploration
Luigi Nardi, David Koeplinger, Kunle Olukotun
Multi-objective optimization is a crucial matter in computer systems design space exploration because real-world applications often rely on a trade-off between several objectives.…
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Cody Coleman, Daniel Kang, Deepak Narayanan +7
Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the…