D. Sculley

45 papers A* 12A 2B 1Journal 18Unranked 12
YearRankTypeTitle / Venue / Authors
2025 — conf
Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation.
ICML (Position Papers)
D. Sculley, William Cukierski, Phil Culliton, Sohier Dane, Maggie Demkin, Ryan Holbrook, Addison Howard, Paul Mooney, Walter Reade, Meg Risdal, Nate Keating
2025 J jnl
Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation.
CoRR
D. Sculley, Will Cukierski, Phil Culliton, Sohier Dane, Maggie Demkin, Ryan Holbrook, Addison Howard, Paul Mooney, Walter Reade, Megan Risdal, Nate Keating
2023 J jnl
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.
CoRR
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
2023 A* conf
DataPerf: Benchmarks for Data-Centric AI Development.
NeurIPS
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
2022 J jnl
Plex: Towards Reliability using Pretrained Large Model Extensions.
CoRR
Dustin Tran, Jeremiah Z. 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
2022 J jnl
Underspecification Presents Challenges for Credibility in Modern Machine Learning.
J. Mach. Learn. Res.
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
2021 A conf
A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks.
Interspeech
Shanqing Cai, Lisie Lillianfeld, Katie Seaver, Jordan R. Green, Michael P. Brenner, Philip C. Nelson, D. Sculley
2021 J jnl
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.
CoRR
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. 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
2020 J jnl
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.
CoRR
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
2020 — conf
Fairness is not static: deeper understanding of long term fairness via simulation studies.
FAT*
Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern
2020 A* conf
Population-Based Black-Box Optimization for Biological Sequence Design.
ICML
Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley
2020 J jnl
Population-Based Black-Box Optimization for Biological Sequence Design.
CoRR
Christof Angermüller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy J. Colwell, D. Sculley
2020 J jnl
Underspecification Presents Challenges for Credibility in Modern Machine Learning.
CoRR
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
2019 — conf
AutoGraph: Imperative-style Coding with Graph-based Performance.
SysML
Dan Moldovan, James M. Decker, Fei Wang, Andrew A. Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, Alexander B. Wiltschko
2019 J jnl
Avoiding a Tragedy of the Commons in the Peer Review Process.
CoRR
D. Sculley, Jasper Snoek, Alexander B. Wiltschko
2019 A* conf
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.
NeurIPS
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado
2019 — conf
TensorFlow.js: Machine Learning For The Web and Beyond.
SysML
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
2019 J jnl
TensorFlow.js: Machine Learning for the Web and Beyond.
CoRR
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
2018 J jnl
AutoGraph: Imperative-style Coding with Graph-based Performance.
CoRR
Dan Moldovan, James M. Decker, Fei Wang, Andrew A. Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, Alexander B. Wiltschko
2018 J jnl
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity.
CoRR
Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley
2018 — conf
Winner's Curse? On Pace, Progress, and Empirical Rigor.
ICLR (Workshop)
D. Sculley, Jasper Snoek, Alexander B. Wiltschko, Ali Rahimi
2017 J jnl
Direct-Manipulation Visualization of Deep Networks.
CoRR
Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg
2017 A* conf
Google Vizier: A Service for Black-Box Optimization.
KDD
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, D. Sculley
2017 A* conf
Learning to Count Mosquitoes for the Sterile Insect Technique.
KDD
Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel E. Newburger, Ryan Poplin, Tiantian Zha, D. Sculley
2017 A* conf
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks.
KDD
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
2017 J jnl
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks.
CoRR
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
2017 — conf
The ML test score: A rubric for ML production readiness and technical debt reduction.
IEEE BigData
Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley
2016 J jnl
AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech.
CoRR
Brian Patton, Yannis Agiomyrgiannakis, Michael Terry, Kevin W. Wilson, Rif A. Saurous, D. Sculley
2015 — conf
Hidden Technical Debt in Machine Learning Systems.
NIPS
D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, Dan Dennison
2013 J jnl
Large-Scale Learning with Less RAM via Randomization
CoRR
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
2013 — conf
Large-Scale Learning with Less RAM via Randomization.
ICML (2)
Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young
2011 A* conf
Detecting adversarial advertisements in the wild.
KDD
D. Sculley, Matthew Eric Otey, Michael Pohl, Bridget Spitznagel, John Hainsworth, Yunkai Zhou
2010 A* conf
Combined regression and ranking.
KDD
D. Sculley
2010 A* conf
Web-scale k-means clustering.
WWW
D. Sculley
2009 A* conf
Predicting bounce rates in sponsored search advertisements.
KDD
D. Sculley, Robert G. Malkin, Sugato Basu, Roberto J. Bayardo
2008 — conf
A Distance-Based Method for Detecting Horizontal Gene Transfer in Whole Genomes.
ISBRA
Xintao Wei, Lenore Cowen, Carla E. Brodley, Arthur Brady, D. Sculley, Donna K. Slonim
2008 — conf
Filtering Email Spam in the Presence of Noisy User Feedback.
CEAS
D. Sculley, Gordon V. Cormack
2008 J jnl
Meaning and mining: the impact of implicit assumptions in data mining for the humanities.
Lit. Linguistic Comput.
D. Sculley, Bradley M. Pasanek
2008 J jnl
Mining millions of metaphors.
Lit. Linguistic Comput.
Bradley M. Pasanek, D. Sculley
2008 — conf
On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments.
CEAS
D. Sculley
2007 — conf
Online Active Learning Methods for Fast Label-Efficient Spam Filtering.
CEAS
D. Sculley
2007 A* conf
Practical learning from one-sided feedback.
KDD
D. Sculley
2007 A conf
Rank Aggregation for Similar Items.
SDM
D. Sculley
2007 A* conf
Relaxed online SVMs for spam filtering.
SIGIR
D. Sculley, Gabriel Wachman
2006 B conf
Compression and Machine Learning: A New Perspective on Feature Space Vectors.
DCC
D. Sculley, Carla E. Brodley