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In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems.
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Scaling big data mining infrastructure: the Twitter experience
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A survey on concept drift adaptation
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Online transfer learning
Peilin Zhao, Steven CH Hoi, Jialei Wang, and Bin Li · 2014
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Federated optimization: Distributed optimization beyond the datacenter
Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage · 2015
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Hidden technical debt in machine learning systems
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
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IoT-based big data storage systems in cloud computing: perspectives and challenges
Hongming Cai, Boyi Xu, Lihong Jiang, and Athanasios V Vasilakos · 2016
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Detecting data errors: Where are we and what needs to be done?
Ziawasch Abedjan, Xu Chu, Dong Deng, Raul Castro Fernandez, Ihab F. Ilyas, Mourad Ouzzani, Paolo Papotti, Michael Stonebraker, and Nan Tang · 2016
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Firebird: Predicting fire risk and prioritizing fire inspections in Atlanta
Michael Madaio, Shang-Tse Chen, Oliver L. Haimson, Wenwen Zhang, Xiang Cheng, Matthew Hinds-Aldrich, Duen Horng Chau, and Bistra Dilkina · 2016
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Machine learning algorithms in heavy process manufacturing
Karl Hansson, Siril Yella, Mark Dougherty, and Hasan Fleyeh · 2016
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Developing a prediction model for customer churn from electronic banking services using data mining
Abbas Keramati, Hajar Ghaneei, and Seyed Mohammad Mirmohammadi · 2016
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Unbounded Bayesian optimization via regularization
Bobak Shahriari, Alexandre Bouchard-Cote, and Nando Freitas · 2016
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Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O’Neil · 2016
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Embers at 4 years: Experiences operating an open source indicators forecasting system
Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena, Chang-Tien Lu, Anil Vullikanti, et al · 2016
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Values and the fourth industrial revolution: Connecting the dots between value, values, profit and purpose
D Malan et al · 2016
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AzureML: Anatomy of a machine learning service
AML Team · 2016
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TPOT: A tree-based pipeline optimization tool for automating machine learning
Randal S Olson and Jason H Moore · 2016
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Machine Learning: The Power and Promise of Computers that Learn by Example: an Introduction
Royal Society (Great Britain) · 2017
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What is the team data science process?
WAR Roald Bradley Severtson, L Franks, and G Ericson · 2017
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Weakly-supervised learning of visual relations
Julia Peyre, Josef Sivic, Ivan Laptev, and Cordelia Schmid · 2017
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Data scientists in software teams: State of the art and challenges
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel · 2017
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A review of deep learning methods and applications for unmanned aerial vehicles
Adrian Carrio, Carlos Sampedro, Alejandro Rodriguez-Ramos, and Pascual Campoy · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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Guide for the targeted review of internal models (TRIM)
ECB TRIM Guide · 2017
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Getting smart: Applying continuous delivery to data science to drive car sales, 2017
Arif Wider and Christian Deger · 2017
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Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
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The effect of the general data protection regulation on medical research
John Mark Michael Rumbold and Barbara Pierscionek · 2017
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Why a right to explanation of automated decision-making does not exist in the general data protection regulation
Sandra Wachter, Brent Mittelstadt, and Luciano Floridi · 2017
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The Information Commissioner, the Royal Free, and what we’ve learned, 2017
Mustafa Suleyman and Dominic King · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2017
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Meet Michelangelo: Uber’s machine learning platform
Jeremy Hermann and Mike Del Balso · 2017
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The Data Linter: Lightweight, automated sanity checking for ML data sets
Nick Hynes, D Sculley, and Michael Terry · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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A selective transfer learning method for concept drift adaptation
Ge Xie, Yu Sun, Minlong Lin, and Ke Tang · 2017
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Neil D Lawrence · 2017
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Rules of machine learning: Best practices for ML engineering
Martin Zinkevich · 2017
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Artificial intelligence for the real world
Thomas H. Davenport and Rajeev Ronanki · 2018
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The State of Machine Learning Adoption in the Enterprise
Ben Lorica and Nathan Paco · 2018
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Data lifecycle challenges in production machine learning: a survey
Neoklis Polyzotis, Sudip Roy, Steven Euijong Whang, and Martin Zinkevich · 2018
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Sok: Security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman · 2018
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Hardware-aware machine learning: Modeling and optimization
Diana Marculescu, Dimitrios Stamoulis, and Ermao Cai · 2018
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Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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How can automated machine learning help business data science teams?
Ashkan Ebadi, Yvan Gauthier, Stéphane Tremblay, and Patrick Paul · 2019
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Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
Livia Faes, Siegfried K Wagner, Dun Jack Fu, Xiaoxuan Liu, Edward Korot, Joseph R Ledsam, Trevor Back, Reena Chopra, Nikolas Pontikos, Christoph Kern, et al · 2019
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Failing loudly: An empirical study of methods for detecting dataset shift
Stephan Rabanser, Stephan Günnemann, and Zachary Chase Lipton · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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Model risk management principles for stress testing, 2018
Prudential Regulation Authority · 2018
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Implementation and on-orbit testing results of a space communications cognitive engine
Timothy M Hackett, Sven G Bilén, Paulo Victor Rodrigues Ferreira, Alexander M Wyglinski, Richard C Reinhart, and Dale J Mortensen · 2018
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Deploying machine learning models for public policy: A framework
Klaus Ackermann, Joe Walsh, Adolfo De Unánue, Hareem Naveed, Andrea Navarrete Rivera, Sun-Joo Lee, Jason Bennett, Michael Defoe, Crystal Cody, Lauren Haynes, et al · 2018
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Continual learning in practice
Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle, and Neil Lawrence · 2018
Cited alongside, same era.
Adapting to concept drift in credit card transaction data streams using contextual bandits and decision trees
Dennis Soemers, Tim Brys, Kurt Driessens, Mark Winands, and Ann Nowé · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Modern data oriented programming, 2019
Neil Lawrence · 2019
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Einsatz von Big Data in produzierenden Industrien, 2019
Verein Deutscher Ingenieure (VDI) · 2019
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley · 2020
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Data readiness: Lessons from an emergency
The DELVE Initiative · 2020
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A survey of deep learning applications to autonomous vehicle control
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State of AI report 2020
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