Daniel Golovin
Google Publications
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Machine Learning: The High Interest Credit Card of Technical Debt
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young
SE4ML: Software Engineering for Machine Learning (NIPS 2014 Workshop)
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Ad Click Prediction: a View from the Trenches
H. Brendan McMahan, Gary Holt, D. 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
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2013)
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Large-Scale Learning with Less RAM via Randomization
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Proceedings of the 30 International Conference on Machine Learning (ICML) (2013), pp. 10
Previous Publications
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Hidden Technical Debt in Machine Learning Systems
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
NIPS (2015), pp. 2503-2511
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Improved approximations for two-stage min-cut and shortest path problems under uncertainty
Daniel Golovin, Vineet Goyal, Valentin Polishchuk, R. Ravi 0001, Mikko Sysikaski
Math. Program., vol. 149 (2015), pp. 167-194
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Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments
Daniel Golovin, Andreas Krause, Matthew J. Streeter
CoRR, vol. abs/1407.1082 (2014)
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Sequential Decision Making in Computational Sustainability via Adaptive Submodularity
Andreas Krause, Daniel Golovin, Sarah J. Converse
AI Magazine, vol. 35 (2014), pp. 8-18
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A revealed preference approach to computational complexity in economics
Federico Echenique, Daniel Golovin, Adam Wierman
EC (2011), pp. 101-110
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Adaptive Submodular Optimization under Matroid Constraints
Daniel Golovin, Andreas Krause
CoRR, vol. abs/1101.4450 (2011)
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Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
Daniel Golovin, Andreas Krause
J. Artif. Intell. Res. (JAIR), vol. 42 (2011), pp. 427-486
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Complexity and economics: computational constraints may not matter empirically
Federico Echenique, Daniel Golovin, Adam Wierman
SIGecom Exchanges, vol. 10 (2011), pp. 2-5
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Dynamic Resource Allocation in Conservation Planning
Daniel Golovin, Andreas Krause, Beth Gardner, Sarah J. Converse, Steve Morey
AAAI (2011)
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Randomized Sensing in Adversarial Environments
Andreas Krause, Alex Roper, Daniel Golovin
IJCAI (2011), pp. 2133-2139
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Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Daniel Golovin, Andreas Krause
COLT (2010), pp. 333-345
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Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Daniel Golovin, Andreas Krause
CoRR, vol. abs/1003.3967 (2010)
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Near-Optimal Bayesian Active Learning with Noisy Observations
Daniel Golovin, Andreas Krause, Debajyoti Ray
NIPS (2010), pp. 766-774
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Near-Optimal Bayesian Active Learning with Noisy Observations
Daniel Golovin, Andreas Krause, Debajyoti Ray
CoRR, vol. abs/1010.3091 (2010)
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Online Distributed Sensor Selection
Daniel Golovin, Matthew Faulkner, Andreas Krause
CoRR, vol. abs/1002.1782 (2010)
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Online distributed sensor selection
Daniel Golovin, Matthew Faulkner, Andreas Krause
IPSN (2010), pp. 220-231
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The B-Skip-List: A Simpler Uniquely Represented Alternative to B-Trees
CoRR, vol. abs/1005.0662 (2010)
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B-Treaps: A Uniquely Represented Alternative to B-Trees
ICALP (1) (2009), pp. 487-499
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Online Learning of Assignments
Matthew J. Streeter, Daniel Golovin, Andreas Krause
NIPS (2009), pp. 1794-1802
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Online Learning of Assignments that Maximize Submodular Functions
Daniel Golovin, Andreas Krause, Matthew J. Streeter
CoRR, vol. abs/0908.0772 (2009)
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Simultaneous source location
Konstantin Andreev, Charles Garrod, Daniel Golovin, Bruce M. Maggs, Adam Meyerson
ACM Transactions on Algorithms, vol. 6 (2009)
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All-Norms and All-L_p-Norms Approximation Algorithms
Daniel Golovin, Anupam Gupta, Amit Kumar 0001, Kanat Tangwongsan
FSTTCS (2008), pp. 199-210
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An Online Algorithm for Maximizing Submodular Functions
Matthew J. Streeter, Daniel Golovin
NIPS (2008), pp. 1577-1584
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Uniquely Represented Data Structures for Computational Geometry
Guy E. Blelloch, Daniel Golovin, Virginia Vassilevska
SWAT (2008), pp. 17-28
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Combining Multiple Heuristics Online
Matthew J. Streeter, Daniel Golovin, Stephen F. Smith
AAAI (2007), pp. 1197-1203
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More expressive market models and the future of combinatorial auctions
SIGecom Exchanges, vol. 7 (2007), pp. 55-57
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Restart Schedules for Ensembles of Problem Instances
Matthew J. Streeter, Daniel Golovin, Stephen F. Smith
AAAI (2007), pp. 1204-1210
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Stochastic packing-market planning
EC (2007), pp. 172-181
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Strongly History-Independent Hashing with Applications
Guy E. Blelloch, Daniel Golovin
FOCS (2007), pp. 272-282
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Approximating the k-multicut problem
Daniel Golovin, Viswanath Nagarajan, Mohit Singh
SODA (2006), pp. 621-630
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Pay Today for a Rainy Day: Improved Approximation Algorithms for Demand-Robust Min-Cut and Shortest Path Problems
Daniel Golovin, Vineet Goyal, R. Ravi 0001
STACS (2006), pp. 206-217
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Quorum placement in networks: minimizing network congestion
Daniel Golovin, Anupam Gupta, Bruce M. Maggs, Florian Oprea, Michael K. Reiter
PODC (2006), pp. 16-25









