Compression and Machine Learning: A New Perspective on Feature Space Vectors. - D. Sculley; Carla E. Brodley (DCC, 2006)

Beyond Digital Incunabula: Modeling the Next Generation of Digital Libraries. - Gregory R. Crane; David Bamman; Lisa Cerrato; Alison Jones; David M. Mimno; Adrian Packel; David Sculley; Gabriel Weaver (ECDL, 2006)

Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers. - David Sculley; Gabriel Wachman; Carla E. Brodley (TREC, 2006)

Online Active Learning Methods for Fast Label-Efficient Spam Filtering. - D. Sculley (CEAS, 2007)

Practical learning from one-sided feedback. - D. Sculley (KDD, 2007)

Rank Aggregation for Similar Items. - D. Sculley (SDM, 2007)

Relaxed online SVMs for spam filtering. - D. Sculley; Gabriel Wachman (SIGIR, 2007)

Relaxed Online SVMs in the TREC Spam Filtering Track. - David Sculley; Gabriel Wachman (TREC, 2007)

On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments. - D. Sculley (CEAS, 2008)

Filtering Email Spam in the Presence of Noisy User Feedback. - D. Sculley; Gordon V. Cormack (CEAS, 2008)

A Distance-Based Method for Detecting Horizontal Gene Transfer in Whole Genomes. - Xintao Wei; Lenore Cowen; Carla E. Brodley; Arthur Brady; D. Sculley; Donna K. Slonim (ISBRA, 2008)

Mining millions of metaphors. - Bradley M. Pasanek; D. Sculley (Lit. Linguistic Comput., 2008)

Meaning and mining: the impact of implicit assumptions in data mining for the humanities. - D. Sculley; Bradley M. Pasanek (Lit. Linguistic Comput., 2008)

Predicting bounce rates in sponsored search advertisements. - D. Sculley; Robert G. Malkin; Sugato Basu; Roberto J. Bayardo (KDD, 2009)

Combined regression and ranking. - D. Sculley (KDD, 2010)

Web-scale k-means clustering. - D. Sculley (WWW, 2010)

Detecting adversarial advertisements in the wild. - D. Sculley; Matthew Eric Otey; Michael Pohl; Bridget Spitznagel; John Hainsworth; Yunkai Zhou (KDD, 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)

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)

AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech. - Brian Patton; Yannis Agiomyrgiannakis; Michael Terry; Kevin W. Wilson; Rif A. Saurous; D. Sculley (CoRR, 2016)

The ML test score: A rubric for ML production readiness and technical debt reduction. - Eric Breck; Shanqing Cai; Eric Nielsen; Michael Salib; D. Sculley (IEEE BigData, 2017)

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks. - 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; et al. (KDD, 2017)

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

Learning to Count Mosquitoes for the Sterile Insect Technique. - Yaniv Ovadia; Yoni Halpern; Dilip Krishnan; Josh Livni; Daniel E. Newburger; Ryan Poplin; Tiantian Zha; D. Sculley (KDD, 2017)

Direct-Manipulation Visualization of Deep Networks. - Daniel Smilkov; Shan Carter; D. Sculley; Fernanda B. Viégas; Martin Wattenberg (CoRR, 2017)

Winner's Curse? On Pace, Progress, and Empirical Rigor. - D. Sculley; Jasper Snoek; Alexander B. Wiltschko; Ali Rahimi (ICLR (Workshop), 2018)

BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity. - Alexey A. Gritsenko; Alex D'Amour; James Atwood; Yoni Halpern; D. Sculley (CoRR, 2018)

AutoGraph: Imperative-style Coding with Graph-based Performance. - Dan Moldovan; James M. Decker; Fei Wang 046; Andrew A. Johnson 002; Brian K. Lee; Zachary Nado; D. Sculley; Tiark Rompf; Alexander B. Wiltschko (SysML, 2019)

TensorFlow.js: Machine Learning For The Web and Beyond. - Daniel Smilkov; Nikhil Thorat; Yannick Assogba; Ann Yuan; Nick Kreeger; Ping Yu; Kangyi Zhang; Shanqing Cai; Eric Nielsen; David Soergel; Stan Bileschi; Michael Terry; et al. (SysML, 2019)

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. - Jasper Snoek; Yaniv Ovadia; Emily Fertig; Balaji Lakshminarayanan; Sebastian Nowozin; D. Sculley; Joshua V. Dillon; Jie Ren 006; Zachary Nado (NeurIPS, 2019)

Avoiding a Tragedy of the Commons in the Peer Review Process. - D. Sculley; Jasper Snoek; Alexander B. Wiltschko (CoRR, 2019)

Fair treatment allocations in social networks. - James Atwood; Hansa Srinivasan; Yoni Halpern; David Sculley (CoRR, 2019)

Fairness is not static: deeper understanding of long term fairness via simulation studies. - Alexander D'Amour; Hansa Srinivasan; James Atwood; Pallavi Baljekar; D. Sculley; Yoni Halpern (FAT*, 2020)

Population-Based Black-Box Optimization for Biological Sequence Design. - Christof Angermüller; David Belanger 002; Andreea Gane; Zelda Mariet; David Dohan; Kevin Murphy 002; Lucy J. Colwell; D. Sculley (ICML, 2020)

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. - Zachary Nado; Shreyas Padhy; D. Sculley; Alexander D'Amour; Balaji Lakshminarayanan; Jasper Snoek (CoRR, 2020)

A Voice-Activated Switch for Persons with Motor and Speech Impairments: Isolated-Vowel Spotting Using Neural Networks. - Shanqing Cai; Lisie Lillianfeld; Katie Seaver; Jordan R. Green; Michael P. Brenner; Philip C. Nelson; D. Sculley (Interspeech, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - 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; et al. (CoRR, 2021)

Plex: Towards Reliability using Pretrained Large Model Extensions. - Dustin Tran; Jeremiah Z. Liu; Michael W. Dusenberry; Du Phan; Mark Collier; Jie Ren 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - 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; et al. (J. Mach. Learn. Res., 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - 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; et al. (NeurIPS, 2023)

Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models. - 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; et al. (CoRR, 2023)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)

Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation. - D. Sculley; Will Cukierski; Phil Culliton; Sohier Dane; Maggie Demkin; Ryan Holbrook; Addison Howard; Paul Mooney; Walter Reade; Megan Risdal; Nate Keating (CoRR, 2025)

gpt-oss-120b & gpt-oss-20b Model Card. - Sandhini Agarwal; Lama Ahmad; Jason Ai; Sam Altman; Andy Applebaum; Edwin Arbus; Rahul K. Arora; Yu Bai; Bowen Baker; Haiming Bao; Boaz Barak; Ally Bennett; et al. (CoRR, 2025)