150 citations · 243 across the 5 of their papers we have counts for
5 papers
Pre-training helps Bayesian optimization too
Zi Wang, George E. Dahl, Kevin Swersky +6
Bayesian optimization (BO) has become a popular strategy for global optimization of many expensive real-world functions. Contrary to a common belief that BO is suited to optimizing…
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…
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Ryan Prescott Adams, George E. Dahl, Iain Murray
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associ- ated with pairwise relationships, Finding use in collaborative Filtering, computational bi-…
Multi-task Neural Networks for QSAR Predictions
George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of lat…
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes
Ryan Prescott Adams, George E. Dahl, Iain Murray
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships, finding use in collaborative filtering, computational biolog…