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D. Sculley

David Sculley

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affiliation: Tufts University, Medford, USA

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2018
[i5]
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  - journals/corr/abs-1810-08061
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Dan Moldovan, James M. Decker, Fei Wang, Andrew A. Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, Alexander B. Wiltschko:
AutoGraph: Imperative-style Coding with Graph-based Performance. CoRR abs/1810.08061 (2018)
2017
[c22]
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  - conf/bigdataconf/BreckCNSS17
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Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley:
The ML test score: A rubric for ML production readiness and technical debt reduction. BigData 2017: 1123-1132
[c21]
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Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley:
Google Vizier: A Service for Black-Box Optimization. KDD 2017: 1487-1495
[c20]
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Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie:
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks. KDD 2017: 1763-1771
[c19]
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Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel Newburger, Ryan Poplin, Tiantian Zha, D. Sculley:
Learning to Count Mosquitoes for the Sterile Insect Technique. KDD 2017: 1943-1949
[i4]
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  - journals/corr/abs-1708-02637
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Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie:
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks. CoRR abs/1708.02637 (2017)
[i3]
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  - journals/corr/abs-1708-03788
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Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg:
Direct-Manipulation Visualization of Deep Networks. CoRR abs/1708.03788 (2017)
2016
[i2]
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  - journals/corr/PattonATWSS16
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Brian Patton, Yannis Agiomyrgiannakis, Michael Terry, Kevin W. Wilson, Rif A. Saurous, D. Sculley:
AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech. CoRR abs/1611.09207 (2016)
2015
[c18]
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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
2013
[c17]
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Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. ICML (2) 2013: 325-333
[c16]
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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
[i1]
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  - journals/corr/abs-1303-4664
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Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. CoRR abs/1303.4664 (2013)
2011
[c15]
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  - conf/kdd/SculleyOPSHZ11
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D. Sculley, Matthew Eric Otey, Michael Pohl, Bridget Spitznagel, John Hainsworth, Yunkai Zhou:
Detecting adversarial advertisements in the wild. KDD 2011: 274-282
2010
[c14]
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D. Sculley:
Combined regression and ranking. KDD 2010: 979-988
[c13]
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  - conf/www/Sculley10
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D. Sculley:
Web-scale k-means clustering. WWW 2010: 1177-1178

2000 – 2009

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2009
[c12]
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D. Sculley, Robert G. Malkin, Sugato Basu, Roberto J. Bayardo:
Predicting bounce rates in sponsored search advertisements. KDD 2009: 1325-1334
2008
[j2]
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  - journals/lalc/PasanekS08
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Bradley M. Pasanek, D. Sculley:
Mining millions of metaphors. LLC 23(3): 345-360 (2008)
[j1]
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D. Sculley, Bradley M. Pasanek:
Meaning and mining: the impact of implicit assumptions in data mining for the humanities. LLC 23(4): 409-424 (2008)
[c11]
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D. Sculley:
On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments. CEAS 2008
[c10]
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D. Sculley, Gordon V. Cormack:
Filtering Email Spam in the Presence of Noisy User Feedback. CEAS 2008
[c9]
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  - conf/isbra/WeiCBBSS08
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Xintao Wei, Lenore Cowen, Carla E. Brodley, Arthur Brady, D. Sculley, Donna K. Slonim:
A Distance-Based Method for Detecting Horizontal Gene Transfer in Whole Genomes. ISBRA 2008: 26-37
2007
[c8]
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D. Sculley:
Online Active Learning Methods for Fast Label-Efficient Spam Filtering. CEAS 2007
[c7]
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D. Sculley:
Practical learning from one-sided feedback. KDD 2007: 609-618
[c6]
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D. Sculley:
Rank Aggregation for Similar Items. SDM 2007: 587-592
[c5]
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D. Sculley, Gabriel Wachman:
Relaxed online SVMs for spam filtering. SIGIR 2007: 415-422
[c4]
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David Sculley, Gabriel Wachman:
Relaxed Online SVMs in the TREC Spam Filtering Track. TREC 2007
2006
[c3]
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  - conf/dcc/SculleyB06
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D. Sculley, Carla E. Brodley:
Compression and Machine Learning: A New Perspective on Feature Space Vectors. DCC 2006: 332-332
[c2]
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  - conf/ercimdl/CraneBCJMPSW06
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Gregory R. Crane, David Bamman, Lisa Cerrato, Alison Jones, David M. Mimno, Adrian Packel, David Sculley, Gabriel Weaver:
Beyond Digital Incunabula: Modeling the Next Generation of Digital Libraries. ECDL 2006: 353-366
[c1]
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David Sculley, Gabriel Wachman, Carla E. Brodley:
Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers. TREC 2006

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