D. Sculley

发表

Machine Learning: The High Interest Credit Card of Technical Debt

W. B. Roberts, D. Sculley, Gary Holt, 2014 .

Google Vizier: A Service for Black-Box Optimization

D. Sculley, Greg Kochanski, John Karro, 2017, KDD.

Large-Scale Learning with Less RAM via Randomization

D. Sculley, H. Brendan McMahan, Michael Young, 2013, ICML.

Ad click prediction: a view from the trenches

Martin Wattenberg, D. Sculley, Dan Liu, 2013, KDD.

Web-scale k-means clustering

D. Sculley, D. Sculley, 2010, WWW '10.

TensorFlow.js: Machine Learning for the Web and Beyond pdf

Martin Wattenberg, D. Sculley, Ping Yu, 2019, MLSys.

Hidden Technical Debt in Machine Learning Systems

D. Sculley, Jean-François Crespo, Gary Holt, 2015, NIPS.

Relaxed online SVMs for spam filtering

D. Sculley, Gabriel Wachman, D. Sculley, 2007, SIGIR.

Detecting adversarial advertisements in the wild

D. Sculley, Matthew Eric Otey, Yunkai Zhou, 2011, KDD.

Learning to Count Mosquitoes for the Sterile Insect Technique

D. Sculley, Dilip Krishnan, Yoni Halpern, 2017, KDD.

Winner's Curse? On Pace, Progress, and Empirical Rigor

D. Sculley, Jasper Snoek, Ali Rahimi, 2018, ICLR.

Spam Filtering Using Inexact String Matching in Explicit Feature Space with On-Line Linear Classifiers

Carla E. Brodley, D. Sculley, Gabriel Wachman, 2006, TREC.

Mining millions of metaphors

D. Sculley, Bradley M. Pasanek, D. Sculley, 2008, Lit. Linguistic Comput..

Population Based Optimization for Biological Sequence Design

D. Sculley, David Dohan, Andreea Gane, 2020 .

Bayesian Optimization for a Better Dessert

D. Sculley, Greg Kochanski, Subhodeep Moitra, 2017 .

Deep Learning Classifies the Protein Universe

D. Sculley, Brandon Carter, David Belanger, 2019 .

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

D. Sculley, Zachary R. McCaw, Farhad Hormozdiari, 2020, American journal of human genetics.

AutoGraph: Imperative-style Coding with Graph-based Performance

D. Sculley, Fei Wang, Tiark Rompf, 2018, MLSys.

Plex: Towards Reliability using Pretrained Large Model Extensions

Michael W. Dusenberry, Joost R. van Amersfoort, E. Kelly Buchanan, 2022, ArXiv.

Complex Models Erode Boundaries • Data Dependencies Cost More than Code Dependencies • Feedback Loops

Steven Euijong Whang, D. Sculley, Gary Holt, 2018 .

Fairness is not static: deeper understanding of long term fairness via simulation studies

Alexander D'Amour, D. Sculley, Pallavi Baljekar, 2020, FAT*.

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Sebastian Nowozin, Jasper Snoek, Jie Ren, 2019, NeurIPS.

Underspecification Presents Challenges for Credibility in Modern Machine Learning pdf

Alexander D'Amour, Andrea Montanari, Xiaohua Zhai, 2020, J. Mach. Learn. Res..

Using deep learning to annotate the protein universe

D. Sculley, Brandon Carter, Lucy J. Colwell, 2019, Nature Biotechnology.

Meaning and mining: the impact of implicit assumptions in data mining for the humanities

D. Sculley, Bradley M. Pasanek, D. Sculley, 2008, Lit. Linguistic Comput..

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

Ryan P. Adams, David Belanger, Jennifer N Wei, 2018, ACS central science.

Beyond Digital Incunabula: Modeling the Next Generation of Digital Libraries

D. Sculley, David Bamman, Gregory R. Crane, 2006, ECDL.

No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

D. Sculley, Yoni Halpern, Jimbo Wilson, 2017, 1711.08536.

AutoGraph: Imperative-style Coding with Graph-based Performance pdf

Fei Wang, D. Sculley, Tiark Rompf, 2018, SysML.

A Distance-Based Method for Detecting Horizontal Gene Transfer in Whole Genomes

Xintao Wei, Lenore Cowen, Carla E. Brodley, 2008, ISBRA.

The Inclusive Images Competition

D. Sculley, Sergey I. Nikolenko, Pallavi Baljekar, 2019 .

Avoiding a Tragedy of the Commons in the Peer Review Process pdf

D. Sculley, Jasper Snoek, Alexander B. Wiltschko, 2018, ArXiv.

The Data Linter: Lightweight Automated Sanity Checking for ML Data Sets

D. Sculley, Michael Terry, Nick Hynes, 2017 .

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

D. Sculley, Jasper Snoek, Balaji Lakshminarayanan, 2020, ArXiv.

Population-Based Black-Box Optimization for Biological Sequence Design

David Dohan, Andreea Gane, David Belanger, 2020, ICML.

The ML test score: A rubric for ML production readiness and technical debt reduction

D. Sculley, Shanqing Cai, Eric Nielsen, 2017, 2017 IEEE International Conference on Big Data (Big Data).

TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning

D. Sculley, Shanqing Cai, Eric Nielsen, 2016 .

Online Active Learning Methods for Fast Label-Efficient Spam Filtering

D. Sculley, D. Sculley, 2007, CEAS.

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning

Michael W. Dusenberry, Tim G. J. Rudner, Angelos Filos, 2021, ArXiv.

Fair treatment allocations in social networks pdf

Yoni Halpern, James Atwood, D Sculley, 2019, ArXiv.

AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech pdf

D. Sculley, Kevin W. Wilson, Brian Patton, 2016, ArXiv.

Combined regression and ranking

D. Sculley, D. Sculley, 2010, KDD.

Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models

Hannah Rose Kirk, D. Sculley, V. Reddi, 2023, ArXiv.

Predicting bounce rates in sponsored search advertisements

D. Sculley, Roberto J. Bayardo, Sugato Basu, 2009, KDD.

Rank Aggregation for Similar Items

D. Sculley, D. Sculley, 2007, SDM.

Large Scale Learning to Rank

D. Sculley, D. Sculley, 2009 .

Advances in online learning-based spam filtering

Carla E. Brodley, D. Sculley, D. Sculley, 2008 .

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

D. Sculley, Heng-Tze Cheng, Jianwei Xie, 2017, KDD.

Compression and machine learning: a new perspective on feature space vectors

Carla E. Brodley, D. Sculley, D. Sculley, 2006, Data Compression Conference (DCC'06).

Survey of Scientific Rigor Studied in Machine Learning

H. Larochelle, D. Sculley, Gary Holt, 2023 .

Direct-Manipulation Visualization of Deep Networks pdf

Martin Wattenberg, D. Sculley, Fernanda B. Viégas, 2017, ArXiv.

Predicting Electron-Ionization Mass Spectrometry using Neural Networks

D. Sculley, Ryan P. Adams, David Belanger, 2018 .