Quorum placement in networks: minimizing network congestion. - Daniel Golovin; Anupam Gupta 001; Bruce M. Maggs; Florian Oprea; Michael K. Reiter (PODC, 2006)

Approximating the k-multicut problem. - Daniel Golovin; Viswanath Nagarajan; Mohit Singh (SODA, 2006)

Pay Today for a Rainy Day: Improved Approximation Algorithms for Demand-Robust Min-Cut and Shortest Path Problems. - Daniel Golovin; Vineet Goyal; R. Ravi 001 (STACS, 2006)

Combining Multiple Heuristics Online. - Matthew J. Streeter; Daniel Golovin; Stephen F. Smith (AAAI, 2007)

Restart Schedules for Ensembles of Problem Instances. - Matthew J. Streeter; Daniel Golovin; Stephen F. Smith (AAAI, 2007)

Strongly History-Independent Hashing with Applications. - Guy E. Blelloch; Daniel Golovin (FOCS, 2007)

Stochastic packing-market planning. - Daniel Golovin (EC, 2007)

More expressive market models and the future of combinatorial auctions. - Daniel Golovin (SIGecom Exch., 2007)

All-Norms and All-L_p-Norms Approximation Algorithms. - Daniel Golovin; Anupam Gupta 001; Amit Kumar 001; Kanat Tangwongsan (FSTTCS, 2008)

An Online Algorithm for Maximizing Submodular Functions. - Matthew J. Streeter; Daniel Golovin (NIPS, 2008)

Uniquely Represented Data Structures for Computational Geometry. - Guy E. Blelloch; Daniel Golovin; Virginia Vassilevska (SWAT, 2008)

B-Treaps: A Uniquely Represented Alternative to B-Trees. - Daniel Golovin (ICALP (1), 2009)

Online Learning of Assignments. - Matthew J. Streeter; Daniel Golovin; Andreas Krause 001 (NIPS, 2009)

Online Learning of Assignments that Maximize Submodular Functions - Daniel Golovin; Andreas Krause 001; Matthew J. Streeter (CoRR, 2009)

Simultaneous source location. - Konstantin Andreev; Charles Garrod; Daniel Golovin; Bruce M. Maggs; Adam Meyerson (ACM Trans. Algorithms, 2009)

Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization. - Daniel Golovin; Andreas Krause 001 (COLT, 2010)

Online distributed sensor selection. - Daniel Golovin; Matthew Faulkner 001; Andreas Krause 001 (IPSN, 2010)

Near-Optimal Bayesian Active Learning with Noisy Observations. - Daniel Golovin; Andreas Krause 001; Debajyoti Ray (NIPS, 2010)

The B-Skip-List: A Simpler Uniquely Represented Alternative to B-Trees - Daniel Golovin (CoRR, 2010)

Dynamic Resource Allocation in Conservation Planning. - Daniel Golovin; Andreas Krause 001; Beth Gardner; Sarah J. Converse; Steve Morey (AAAI, 2011)

Randomized Sensing in Adversarial Environments. - Andreas Krause 001; Alex Roper; Daniel Golovin (IJCAI, 2011)

A revealed preference approach to computational complexity in economics. - Federico Echenique; Daniel Golovin; Adam Wierman (EC, 2011)

Adaptive Submodular Optimization under Matroid Constraints - Daniel Golovin; Andreas Krause 001 (CoRR, 2011)

Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization. - Daniel Golovin; Andreas Krause 001 (J. Artif. Intell. Res., 2011)

Complexity and economics: computational constraints may not matter empirically. - Federico Echenique; Daniel Golovin; Adam Wierman (SIGecom Exch., 2011)

Large-Scale Learning with Less RAM via Randomization. - Daniel Golovin; D. Sculley; H. Brendan McMahan; Michael Young (ICML (2), 2013)

Ad click prediction: a view from the trenches. - 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; et al. (KDD, 2013)

Submodular Function Maximization. - Andreas Krause 001; Daniel Golovin (Tractability, 2014)

Sequential Decision Making in Computational Sustainability via Adaptive Submodularity. - Andreas Krause 001; Daniel Golovin; Sarah J. Converse (AI Mag., 2014)

Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments. - Daniel Golovin; Andreas Krause 001; Matthew J. Streeter (CoRR, 2014)

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)

Improved approximations for two-stage min-cut and shortest path problems under uncertainty. - Daniel Golovin; Vineet Goyal; Valentin Polishchuk; R. Ravi 001; Mikko Sysikaski (Math. Program., 2015)

Google Vizier: A Service for Black-Box Optimization. - Daniel Golovin; Benjamin Solnik; Subhodeep Moitra; Greg Kochanski; John Karro; D. Sculley (KDD, 2017)

Gradientless Descent: High-Dimensional Zeroth-Order Optimization. - Daniel Golovin; John Karro; Greg Kochanski; Chansoo Lee; Xingyou Song; Qiuyi (Richard) Zhang (ICLR, 2020)

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization. - Qiuyi (Richard) Zhang; Daniel Golovin (ICML, 2020)

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. - Xingyou Song; Sagi Perel; Chansoo Lee; Greg Kochanski; Daniel Golovin (AutoML, 2022)

SmartChoices: Augmenting Software with Learned Implementations. - Daniel Golovin; Gábor Bartók; Eric Chen; Emily Donahue; Tzu-Kuo Huang; Efi Kokiopoulou; Ruoyan Qin; Nikhil Sarda; Justin Sybrandt; Vincent Tjeng (CoRR, 2023)

The Vizier Gaussian Process Bandit Algorithm. - Xingyou Song; Qiuyi Zhang 001; Chansoo Lee; Emily Fertig; Tzu-Kuo Huang; Lior Belenki; Greg Kochanski; Setareh Ariafar; Srinivas Vasudevan; Sagi Perel; Daniel Golovin (CoRR, 2024)