8 citations · 31 across the 13 of their papers we have counts for
12 papers · 1 filter
Pitfalls and Remedies for Multi-Task Bayesian Optimization
Carl Hvarfner, Sam Daulton, Max Balandat +1
Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We r…
-PFN: Fast Entropy Search via In-Context Learning
Herilalaina Rakotoarison, Steven Adriaensen, Tom Viering +4
Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration-exploitation framework for Bayesian optimization (BO). However, their practic…
Informed Initialization for Bayesian Optimization and Active Learning
Carl Hvarfner, David Eriksson, Eytan Bakshy +1
Bayesian Optimization is a widely used method for optimizing expensive black-box functions, relying on probabilistic surrogate models such as Gaussian Processes. The quality of the…
Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing
Erik Hellsten, Carl Hvarfner, Leonard Papenmeier +1
Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to…
CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Jacob O. Tørring, Carl Hvarfner, Luigi Nardi +1
Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for…
Vanilla Bayesian Optimization Performs Great in High Dimensions
Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi
High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms. Spurred by the curse of dimensionality, a large collection of algorithms…
A General Framework for User-Guided Bayesian Optimization
Carl Hvarfner, Frank Hutter, Luigi Nardi
The optimization of expensive-to-evaluate black-box functions is prevalent in various scientific disciplines. Bayesian optimization is an automatic, general and sample-efficient me…
High-dimensional Bayesian Optimization with Group Testing
Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier +1
Bayesian optimization is an effective method for optimizing expensive-to-evaluate black-box functions. High-dimensional problems are particularly challenging as the surrogate model…
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…
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…