207 citations · 435 across the 12 of their papers we have counts for
9 papers · 1 filter
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization
Xingyou Song, Sagi Perel, Chansoo Lee +2
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts. To operate at the sc…
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Daniel Golovin, Qiuyi Zhang
Single-objective black box optimization (also known as zeroth-order optimization) is the process of minimizing a scalar objective , given evaluations at adaptively chosen inp…
Gradientless Descent: High-Dimensional Zeroth-Order Optimization
Daniel Golovin, John Karro, Greg Kochanski +3
Zeroth-order optimization is the process of minimizing an objective , given oracle access to evaluations at adaptively chosen inputs . In this paper, we present two simple…
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments
Daniel Golovin, Andreas Krause, Matthew Streeter
Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize the value of the ranking? These app…
Large-Scale Learning with Less RAM via Randomization
Daniel Golovin, D. Sculley, H. Brendan McMahan +1
We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to stan…
Near-Optimal Bayesian Active Learning with Noisy Observations
Daniel Golovin, Andreas Krause, Debajyoti Ray
We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypoth…