Daniel Golovin

Publication Activity (10 Years)

Years Active: 2006-2023
Publications (10 Years): 8

Top Topics

Strength Pareto Evolutionary Algorithm

Machine Learning Systems

Top Venues

Publications

Daniel Golovin, Gábor Bartók, Eric Chen, Emily Donahue, Tzu-Kuo Huang, Efi Kokiopoulou, Ruoyan Qin, Nikhil Sarda, Justin Sybrandt, Vincent Tjeng
SmartChoices: Augmenting Software with Learned Implementations. CoRR (2023)
Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. AutoML (2022)
Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. CoRR (2022)
Daniel Golovin, Qiuyi Zhang
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. CoRR (2020)
Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi Zhang
Gradientless Descent: High-Dimensional Zeroth-Order Optimization. ICLR (2020)
Richard Zhang, Daniel Golovin
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. ICML (2020)
Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi Zhang
Gradientless Descent: High-Dimensional Zeroth-Order Optimization. CoRR (2019)
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley
Google Vizier: A Service for Black-Box Optimization. KDD (2017)
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
Hidden Technical Debt in Machine Learning Systems. NIPS (2015)
Daniel Golovin, Vineet Goyal, Valentin Polishchuk, R. Ravi, Mikko Sysikaski
Improved approximations for two-stage min-cut and shortest path problems under uncertainty. Math. Program. 149 (1-2) (2015)
Andreas Krause, Daniel Golovin, Sarah J. Converse
Sequential Decision Making in Computational Sustainability via Adaptive Submodularity. AI Mag. 35 (2) (2014)
Daniel Golovin, Andreas Krause, Matthew J. Streeter
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments. CoRR (2014)
H. Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica
Ad click prediction: a view from the trenches. KDD (2013)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Large-Scale Learning with Less RAM via Randomization CoRR (2013)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Large-Scale Learning with Less RAM via Randomization. ICML (2) (2013)
Daniel Golovin, Andreas Krause, Beth Gardner, Sarah J. Converse, Steve Morey
Dynamic Resource Allocation in Conservation Planning. AAAI (2011)
Daniel Golovin, Andreas Krause
Adaptive Submodular Optimization under Matroid Constraints CoRR (2011)
Andreas Krause, Alex Roper, Daniel Golovin
Randomized Sensing in Adversarial Environments. IJCAI (2011)
Federico Echenique, Daniel Golovin, Adam Wierman
Complexity and economics: computational constraints may not matter empirically. SIGecom Exch. 10 (1) (2011)
Federico Echenique, Daniel Golovin, Adam Wierman
A revealed preference approach to computational complexity in economics. EC (2011)
Daniel Golovin, Andreas Krause
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. J. Artif. Intell. Res. 42 (2011)
Daniel Golovin, Matthew Faulkner, Andreas Krause
Online Distributed Sensor Selection CoRR (2010)
Daniel Golovin, Matthew Faulkner, Andreas Krause
Online distributed sensor selection. IPSN (2010)
Daniel Golovin
The B-Skip-List: A Simpler Uniquely Represented Alternative to B-Trees CoRR (2010)
Daniel Golovin, Andreas Krause, Debajyoti Ray
Near-Optimal Bayesian Active Learning with Noisy Observations CoRR (2010)
Daniel Golovin, Andreas Krause
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization. COLT (2010)
Daniel Golovin, Andreas Krause
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization CoRR (2010)
Daniel Golovin, Andreas Krause, Debajyoti Ray
Near-Optimal Bayesian Active Learning with Noisy Observations. NIPS (2010)
Konstantin Andreev, Charles Garrod, Daniel Golovin, Bruce M. Maggs, Adam Meyerson
Simultaneous source location. ACM Trans. Algorithms 6 (1) (2009)
Daniel Golovin, Andreas Krause, Matthew J. Streeter
Online Learning of Assignments that Maximize Submodular Functions CoRR (2009)
Matthew J. Streeter, Daniel Golovin, Andreas Krause
Online Learning of Assignments. NIPS (2009)
Daniel Golovin
B-Treaps: A Uniquely Represented Alternative to B-Trees. ICALP (1) (2009)
Daniel Golovin, Anupam Gupta, Amit Kumar, Kanat Tangwongsan
All-Norms and All-L_p-Norms Approximation Algorithms. FSTTCS (2008)
Matthew J. Streeter, Daniel Golovin
An Online Algorithm for Maximizing Submodular Functions. NIPS (2008)
Guy E. Blelloch, Daniel Golovin, Virginia Vassilevska
Uniquely Represented Data Structures for Computational Geometry. SWAT (2008)
Daniel Golovin
Stochastic packing-market planning. EC (2007)
Matthew J. Streeter, Daniel Golovin, Stephen F. Smith
Combining Multiple Heuristics Online. AAAI (2007)
Matthew J. Streeter, Daniel Golovin, Stephen F. Smith
Restart Schedules for Ensembles of Problem Instances. AAAI (2007)
Daniel Golovin
More expressive market models and the future of combinatorial auctions. SIGecom Exch. 7 (1) (2007)
Guy E. Blelloch, Daniel Golovin
Strongly History-Independent Hashing with Applications. FOCS (2007)
Daniel Golovin, Anupam Gupta, Bruce M. Maggs, Florian Oprea, Michael K. Reiter
Quorum placement in networks: minimizing network congestion. PODC (2006)
Daniel Golovin, Vineet Goyal, R. Ravi
Pay Today for a Rainy Day: Improved Approximation Algorithms for Demand-Robust Min-Cut and Shortest Path Problems. STACS (2006)
Daniel Golovin, Viswanath Nagarajan, Mohit Singh
-multicut problem. SODA (2006)