5.7k citations · 8.4k across the 42 of their papers we have counts for
5 papers · 1 filter
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach
Setareh Ariafar, Justin Gilmer, Zachary Nado +3
Black box optimization requires specifying a search space to explore for solutions, e.g. a d-dimensional compact space, and this choice is critical for getting the best results at…
Sparse MoEs meet Efficient Ensembles
James Urquhart Allingham, Florian Wenzel, Zelda E Mariet +10
Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, often exhibit strong performance compared to individual models.…
Pre-trained Gaussian Processes for Bayesian Optimization
Zi Wang, George E. Dahl, Kevin Swersky +5
Bayesian optimization (BO) has become a popular strategy for global optimization of expensive real-world functions. Contrary to a common expectation that BO is suited to optimizing…
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier +23
High-quality estimates of uncertainty and robustness are crucial for numerous real-world applications, especially for deep learning which underlies many deployed ML systems. The ab…
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
Samuel Kim, Peter Y. Lu, Charlotte Loh +3
Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-b…