1 code implementation • ACM (2017) 2017 • Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley
Any sufficiently complex system acts as a black box when it becomes easier to experiment with than to understand.
1 code implementation • 27 Jul 2022 • Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin
Vizier is the de-facto blackbox and hyperparameter optimization service across Google, having optimized some of Google's largest products and research efforts.
no code implementations • 21 Mar 2010 • Daniel Golovin, Andreas Krause
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge.
no code implementations • 3 Jul 2014 • Daniel Golovin, Andreas Krause, Matthew Streeter
How should we dynamically rank information sources to maximize the value of the ranking?
no code implementations • NeurIPS 2010 • Daniel Golovin, Andreas Krause, Debajyoti Ray
In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perform near-optimally.
no code implementations • NeurIPS 2015 • D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly.
no code implementations • NeurIPS 2009 • Matthew Streeter, Daniel Golovin, Andreas Krause
Which ads should we display in sponsored search in order to maximize our revenue?
no code implementations • NeurIPS 2008 • Matthew Streeter, Daniel Golovin
We present an algorithm for solving a broad class of online resource allocation problems.
no code implementations • ICLR 2020 • Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi Zhang
Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$.
no code implementations • ICML 2020 • Daniel Golovin, Qiuyi Zhang
Single-objective black box optimization (also known as zeroth-order optimization) is the process of minimizing a scalar objective $f(x)$, given evaluations at adaptively chosen inputs $x$.
no code implementations • 12 Apr 2023 • Daniel Golovin, Gabor Bartok, Eric Chen, Emily Donahue, Tzu-Kuo Huang, Efi Kokiopoulou, Ruoyan Qin, Nikhil Sarda, Justin Sybrandt, Vincent Tjeng
We are living in a golden age of machine learning.