Search Results for author: Daniel Golovin

Found 11 papers, 2 papers with code

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization

1 code implementation27 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.

Hyperparameter Optimization Transfer Learning

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

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 $x$.

Bayesian Optimization Thompson Sampling

Gradientless Descent: High-Dimensional Zeroth-Order Optimization

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 $x$.

Vocal Bursts Intensity Prediction

Near-Optimal Bayesian Active Learning with Noisy Observations

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.

Active Learning Experimental Design

Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

no code implementations21 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.

Active Learning Marketing +1

Online Learning of Assignments

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?

An Online Algorithm for Maximizing Submodular Functions

no code implementations NeurIPS 2008 Matthew Streeter, Daniel Golovin

We present an algorithm for solving a broad class of online resource allocation problems.

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