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2020 – today
- 2024
[i13]Xingyou Song, Qiuyi Zhang, Chansoo Lee, Emily Fertig, Tzu-Kuo Huang, Lior Belenki, Greg Kochanski, Setareh Ariafar, Srinivas Vasudevan, Sagi Perel, Daniel Golovin:
The Vizier Gaussian Process Bandit Algorithm. CoRR abs/2408.11527 (2024)- 2023
- [i12]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 abs/2304.13033 (2023) - 2022
- [c25]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: 8/1-17 - [i11]Xingyou Song, Sagi Perel, Chansoo Lee, Greg Kochanski, Daniel Golovin:
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. CoRR abs/2207.13676 (2022) - 2020
- [c24]Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi (Richard) Zhang:
Gradientless Descent: High-Dimensional Zeroth-Order Optimization. ICLR 2020 - [c23]Qiuyi (Richard) Zhang, Daniel Golovin:
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. ICML 2020: 11096-11105 - [i10]Daniel Golovin, Qiuyi (Richard) Zhang:
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. CoRR abs/2006.04655 (2020)
2010 – 2019
- 2019
- [i9]Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song, Qiuyi (Richard) Zhang:
Gradientless Descent: High-Dimensional Zeroth-Order Optimization. CoRR abs/1911.06317 (2019) - 2017
- [c22]Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley:
Google Vizier: A Service for Black-Box Optimization. KDD 2017: 1487-1495 - 2015
- [j6]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): 167-194 (2015) - [c21]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: 2503-2511 - 2014
- [j5]Andreas Krause, Daniel Golovin, Sarah J. Converse:
Sequential Decision Making in Computational Sustainability via Adaptive Submodularity. AI Mag. 35(2): 8-18 (2014) - [p1]Andreas Krause, Daniel Golovin:
Submodular Function Maximization. Tractability 2014: 71-104 - [i8]Daniel Golovin, Andreas Krause, Matthew J. Streeter:
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments. CoRR abs/1407.1082 (2014) - 2013
- [c20]Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. ICML (2) 2013: 325-333 - [c19]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: 1222-1230 - [i7]Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. CoRR abs/1303.4664 (2013) - 2011
- [j4]Daniel Golovin, Andreas Krause:
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. J. Artif. Intell. Res. 42: 427-486 (2011) - [j3]Federico Echenique
, Daniel Golovin, Adam Wierman:
Complexity and economics: computational constraints may not matter empirically. SIGecom Exch. 10(1): 2-5 (2011) - [c18]Daniel Golovin, Andreas Krause, Beth Gardner, Sarah J. Converse, Steve Morey:
Dynamic Resource Allocation in Conservation Planning. AAAI 2011 - [c17]Andreas Krause, Alex Roper, Daniel Golovin:
Randomized Sensing in Adversarial Environments. IJCAI 2011: 2133-2139 - [c16]Federico Echenique, Daniel Golovin, Adam Wierman:
A revealed preference approach to computational complexity in economics. EC 2011: 101-110 - [i6]Daniel Golovin, Andreas Krause:
Adaptive Submodular Optimization under Matroid Constraints. CoRR abs/1101.4450 (2011) - 2010
- [c15]Daniel Golovin, Andreas Krause:
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization. COLT 2010: 333-345 - [c14]Daniel Golovin, Matthew Faulkner, Andreas Krause:
Online distributed sensor selection. IPSN 2010: 220-231 - [c13]Daniel Golovin, Andreas Krause, Debajyoti Ray:
Near-Optimal Bayesian Active Learning with Noisy Observations. NIPS 2010: 766-774 - [i5]Daniel Golovin, Matthew Faulkner, Andreas Krause:
Online Distributed Sensor Selection. CoRR abs/1002.1782 (2010) - [i4]Daniel Golovin, Andreas Krause:
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization. CoRR abs/1003.3967 (2010) - [i3]Daniel Golovin:
The B-Skip-List: A Simpler Uniquely Represented Alternative to B-Trees. CoRR abs/1005.0662 (2010) - [i2]Daniel Golovin, Andreas Krause, Debajyoti Ray:
Near-Optimal Bayesian Active Learning with Noisy Observations. CoRR abs/1010.3091 (2010)
2000 – 2009
- 2009
- [j2]Konstantin Andreev, Charles Garrod, Daniel Golovin, Bruce M. Maggs, Adam Meyerson:
Simultaneous source location. ACM Trans. Algorithms 6(1): 16:1-16:17 (2009) - [c12]Daniel Golovin:
B-Treaps: A Uniquely Represented Alternative to B-Trees. ICALP (1) 2009: 487-499 - [c11]Matthew J. Streeter, Daniel Golovin, Andreas Krause:
Online Learning of Assignments. NIPS 2009: 1794-1802 - [i1]Daniel Golovin, Andreas Krause, Matthew J. Streeter:
Online Learning of Assignments that Maximize Submodular Functions. CoRR abs/0908.0772 (2009) - 2008
- [c10]Daniel Golovin, Anupam Gupta, Amit Kumar, Kanat Tangwongsan:
All-Norms and All-L_p-Norms Approximation Algorithms. FSTTCS 2008: 199-210 - [c9]Matthew J. Streeter, Daniel Golovin:
An Online Algorithm for Maximizing Submodular Functions. NIPS 2008: 1577-1584 - [c8]Guy E. Blelloch, Daniel Golovin, Virginia Vassilevska:
Uniquely Represented Data Structures for Computational Geometry. SWAT 2008: 17-28 - 2007
- [j1]Daniel Golovin:
More expressive market models and the future of combinatorial auctions. SIGecom Exch. 7(1): 55-57 (2007) - [c7]Matthew J. Streeter, Daniel Golovin, Stephen F. Smith:
Combining Multiple Heuristics Online. AAAI 2007: 1197-1203 - [c6]Matthew J. Streeter, Daniel Golovin, Stephen F. Smith:
Restart Schedules for Ensembles of Problem Instances. AAAI 2007: 1204-1210 - [c5]Guy E. Blelloch, Daniel Golovin:
Strongly History-Independent Hashing with Applications. FOCS 2007: 272-282 - [c4]Daniel Golovin:
Stochastic packing-market planning. EC 2007: 172-181 - 2006
- [c3]Daniel Golovin, Anupam Gupta, Bruce M. Maggs, Florian Oprea, Michael K. Reiter:
Quorum placement in networks: minimizing network congestion. PODC 2006: 16-25 - [c2]Daniel Golovin, Viswanath Nagarajan, Mohit Singh:
Approximating the k-multicut problem. SODA 2006: 621-630 - [c1]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: 206-217
Coauthor Index
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last updated on 2024-09-28 02:20 CEST by the dblp team
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