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Semantic ScholarD. Sculley
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- affiliation: Tufts University, Medford, USA
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2020 – today
- 2020
[c27]Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern:
Fairness is not static: deeper understanding of long term fairness via simulation studies. FAT* 2020: 525-534- [i12]Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D. Sculley:
Population-Based Black-Box Optimization for Biological Sequence Design. CoRR abs/2006.03227 (2020) - [i11]Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek:
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR abs/2006.10963 (2020)
2010 – 2019
- 2019
- [c26]Dan Moldovan, James M. Decker, Fei Wang, Andrew A. Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, Alexander B. Wiltschko:
AG: Imperative-style Coding with Graph-based Performance. MLSys 2019 - [c25]Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viégas, Martin Wattenberg:
TensorFlow.js: Machine Learning For The Web and Beyond. MLSys 2019 - [c24]Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado:
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS 2019: 13969-13980 - [i10]Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viégas, Martin Wattenberg:
TensorFlow.js: Machine Learning for the Web and Beyond. CoRR abs/1901.05350 (2019) - [i9]D. Sculley, Jasper Snoek, Alexander B. Wiltschko:
Avoiding a Tragedy of the Commons in the Peer Review Process. CoRR abs/1901.06246 (2019) - [i8]Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek:
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR abs/1906.02530 (2019) - [i7]James Atwood, Hansa Srinivasan, Yoni Halpern, David Sculley:
Fair treatment allocations in social networks. CoRR abs/1911.05489 (2019) - 2018
- [c23]D. Sculley, Jasper Snoek, Alexander B. Wiltschko, Ali Rahimi:
Winner's Curse? On Pace, Progress, and Empirical Rigor. ICLR (Workshop) 2018 - [i6]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) - [i5]Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley:
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity. CoRR abs/1812.06869 (2018) - 2017
- [c22]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]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]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]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]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]Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg:
Direct-Manipulation Visualization of Deep Networks. CoRR abs/1708.03788 (2017) - 2016
- [i2]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]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]Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. ICML (2) 2013: 325-333 - [c16]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]Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young:
Large-Scale Learning with Less RAM via Randomization. CoRR abs/1303.4664 (2013) - 2011
- [c15]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]D. Sculley:
Combined regression and ranking. KDD 2010: 979-988 - [c13]D. Sculley:
Web-scale k-means clustering. WWW 2010: 1177-1178
2000 – 2009
- 2009
- [c12]D. Sculley, Robert G. Malkin, Sugato Basu, Roberto J. Bayardo:
Predicting bounce rates in sponsored search advertisements. KDD 2009: 1325-1334 - 2008
- [j2]Bradley M. Pasanek, D. Sculley:
Mining millions of metaphors. Lit. Linguistic Comput. 23(3): 345-360 (2008) - [j1]D. Sculley, Bradley M. Pasanek:
Meaning and mining: the impact of implicit assumptions in data mining for the humanities. Lit. Linguistic Comput. 23(4): 409-424 (2008) - [c11]D. Sculley:
On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments. CEAS 2008 - [c10]D. Sculley, Gordon V. Cormack:
Filtering Email Spam in the Presence of Noisy User Feedback. CEAS 2008 - [c9]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]D. Sculley:
Online Active Learning Methods for Fast Label-Efficient Spam Filtering. CEAS 2007 - [c7]D. Sculley:
Practical learning from one-sided feedback. KDD 2007: 609-618 - [c6]D. Sculley:
Rank Aggregation for Similar Items. SDM 2007: 587-592 - [c5]D. Sculley, Gabriel Wachman:
Relaxed online SVMs for spam filtering. SIGIR 2007: 415-422 - [c4]David Sculley, Gabriel Wachman:
Relaxed Online SVMs in the TREC Spam Filtering Track. TREC 2007 - 2006
- [c3]D. Sculley, Carla E. Brodley:
Compression and Machine Learning: A New Perspective on Feature Space Vectors. DCC 2006: 332-332 - [c2]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]David Sculley, Gabriel Wachman, Carla E. Brodley:
Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers. TREC 2006
Coauthor Index
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