202 citations · 1k across the 46 of their papers we have counts for
6 papers · 1 filter
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Benjamin Letham, Phillip Guan, Chase Tymms +2
Level set estimation (LSE) is the problem of identifying regions where an unknown function takes values above or below a specified threshold. Active sampling strategies for efficie…
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Samuel Daulton, Maximilian Balandat, Eytan Bakshy
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization (BO) i…
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Benjamin Letham, Roberto Calandra, Akshara Rai +1
Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spac…
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Benjamin Letham, Eytan Bakshy
Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional p…
Practical Transfer Learning for Bayesian Optimization
Matthias Feurer, Benjamin Letham, Frank Hutter +1
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We…
Constrained Bayesian Optimization with Noisy Experiments
Benjamin Letham, Brian Karrer, Guilherme Ottoni +1
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high…