D. Sculley

Publication Activity (10 Years)

Years Active: 2006-2023
Publications (10 Years): 26

Top Topics

Formant Frequencies

Peer Review Process

Machine Learning

Top Venues

Publications

Mark Mazumder, Colby R. Banbury, Xiaozhe Yao, Bojan Karlas, William Gaviria Rojas, Sudnya Frederick Diamos, Greg Diamos, Lynn He, Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Douwe Kiela, David Jurado, David Kanter, Rafael Mosquera, Will Cukierski, Juan Ciro, Lora Aroyo, Bilge Acun, Lingjiao Chen, Mehul Raje, Max Bartolo, Evan Sabri Eyuboglu, Amirata Ghorbani, Emmett D. Goodman, Addison Howard, Oana Inel, Tariq Kane, Christine R. Kirkpatrick, D. Sculley, Tzu-Sheng Kuo, Jonas W. Mueller, Tristan Thrush, Joaquin Vanschoren, Margaret Warren, Adina Williams, Serena Yeung, Newsha Ardalani, Praveen K. Paritosh, Ce Zhang, James Y. Zou, Carole-Jean Wu, Cody Coleman, Andrew Y. Ng, Peter Mattson, Vijay Janapa Reddi
DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS (2023)
Alicia Parrish, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max Bartolo, Oana Inel, Juan Ciro, Rafael Mosquera, Addison Howard, Will Cukierski, D. Sculley, Vijay Janapa Reddi, Lora Aroyo
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models. CoRR (2023)
Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR (2022)
Shanqing Cai, Lisie Lillianfeld, Katie Seaver, Jordan R. Green, Michael P. Brenner, Philip C. Nelson, D. Sculley
A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks. Interspeech (2021)
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR (2021)
Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR (2020)
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)
Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley
Population-Based Black-Box Optimization for Biological Sequence Design. CoRR (2020)
Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley
Population-Based Black-Box Optimization for Biological Sequence Design. ICML (2020)
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)
D. Sculley, Jasper Snoek, Alexander B. Wiltschko
Avoiding a Tragedy of the Commons in the Peer Review Process. CoRR (2019)
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)
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. MLSys (2019)
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 (2019)
Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity. CoRR (2018)
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 (2018)
D. Sculley, Jasper Snoek, Alexander B. Wiltschko, Ali Rahimi
Winner's Curse? On Pace, Progress, and Empirical Rigor. ICLR (Workshop) (2018)
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 (2017)
Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg
Direct-Manipulation Visualization of Deep Networks. CoRR (2017)
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)
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley
Google Vizier: A Service for Black-Box Optimization. KDD (2017)
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)
Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley
The ML test score: A rubric for ML production readiness and technical debt reduction. IEEE BigData (2017)
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 (2016)
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)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Large-Scale Learning with Less RAM via Randomization CoRR (2013)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
Large-Scale Learning with Less RAM via Randomization. ICML (2) (2013)
D. Sculley, Matthew Eric Otey, Michael Pohl, Bridget Spitznagel, John Hainsworth, Yunkai Zhou
Detecting adversarial advertisements in the wild. KDD (2011)
D. Sculley
Web-scale k-means clustering. WWW (2010)
D. Sculley
Combined regression and ranking. KDD (2010)
D. Sculley, Robert G. Malkin, Sugato Basu, Roberto J. Bayardo
Predicting bounce rates in sponsored search advertisements. KDD (2009)
D. Sculley, Gordon V. Cormack
Filtering Email Spam in the Presence of Noisy User Feedback. CEAS (2008)
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)
Bradley M. Pasanek, D. Sculley
Mining millions of metaphors. Lit. Linguistic Comput. 23 (3) (2008)
D. Sculley, Bradley M. Pasanek
Meaning and mining: the impact of implicit assumptions in data mining for the humanities. Lit. Linguistic Comput. 23 (4) (2008)
D. Sculley
On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments. CEAS (2008)
D. Sculley
Practical learning from one-sided feedback. KDD (2007)
D. Sculley
Online Active Learning Methods for Fast Label-Efficient Spam Filtering. CEAS (2007)
D. Sculley, Gabriel Wachman
Relaxed online SVMs for spam filtering. SIGIR (2007)
D. Sculley
Rank Aggregation for Similar Items. SDM (2007)
D. Sculley, Carla E. Brodley
Compression and Machine Learning: A New Perspective on Feature Space Vectors. DCC (2006)