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Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications.
An ADMM Based Framework for AutoML Pipeline Configuration
Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, and Alexander Gray. 2019 · 1905
Earlier work this paper cites.
Snowball sampling: Problems and techniques of chain referral sampling
Patrick Biernacki and Dan Waldorf. 1981 · 1981
Earlier work this paper cites.
XPLAIN: A system for creating and explaining expert consulting programs
William R Swartout. 1983 · 1983
Earlier work this paper cites.
Information needs in technical work settings and their implications for the design of computer tools
Andreas Paepcke. 1996 · 1996
Earlier work this paper cites.
Direct manipulation vs. interface agents
Ben Shneiderman and Pattie Maes. 1997 · 1997
Earlier work this paper cites.
Principles of mixed-initiative user interfaces. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems . 159–166
Eric Horvitz. 1999 · 1999
Earlier work this paper cites.
A model for types and levels of human interaction with automation
Raja Parasuraman, Thomas B Sheridan, and Christopher D Wickens. 2000 · 2000
Earlier work this paper cites.
Turing test: 50 years later
Ayse Pinar Saygin, Ilyas Cicekli, and Varol Akman. 2000 · 2000
Earlier work this paper cites.
Intelligibility and accountability: human considerations in context-aware systems
Victoria Bellotti and Keith Edwards. 2001 · 2001
Earlier work this paper cites.
Ensemble Selection from Libraries of Models. In Proceedings of the Twenty-first International Conference on Machine Learning (ICML ’04)
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes. 2004 · 2004
Earlier work this paper cites.
Trust-inspiring explanation interfaces for recommender systems
Pearl Pu and Li Chen. 2007 · 2007
Earlier work this paper cites.
A survey of explanations in recommender systems. In 2007 IEEE 23rd international conference on data engineering workshop . IEEE, 801–810
Nava Tintarev and Judith Masthoff. 2007 · 2007
Earlier work this paper cites.
Driving automation: learning from aviation about design philosophies
Mark S Young, Neville A Stanton, and Don Harris. 2007 · 2007
Earlier work this paper cites.
Investigating statistical machine learning as a tool for software development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . ACM, 667–676
Kayur Patel, James Fogarty, James A Landay, and Beverly Harrison. 2008 · 2008
Earlier work this paper cites.
Particle swarm model selection
Hugo Jair Escalante, Manuel Montes, and Luis Enrique Sucar. 2009 · 2009
Earlier work this paper cites.
Assessing demand for intelligibility in context-aware applications. In Proceedings of the 11th international conference on Ubiquitous computing . 195–204
Brian Y Lim and Anind K Dey. 2009 · 2009
Earlier work this paper cites.
Why and why not explanations improve the intelligibility of context-aware intelligent systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . 2119–2128
Brian Y Lim, Anind K Dey, and Daniel Avrahami. 2009 · 2009
Earlier work this paper cites.
Maximum-likelihood estimation with a contracting-grid search algorithm
Jacob Y Hesterman, Luca Caucci, Matthew A Kupinski, Harrison H Barrett, and Lars R Furenlid. 2010 · 2010
Earlier work this paper cites.
Communities of practice and social learning systems: the career of a concept
Etienne Wenger. 2010 · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization. In Advances in neural information processing systems . 2546–2554
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011 · 2011
Earlier work this paper cites.
Proactive wrangling: mixed-initiative end-user programming of data transformation scripts. In Proceedings of the 24th annual ACM symposium on User interface software and technology . ACM, 65–74
Philip J Guo, Sean Kandel, Joseph M Hellerstein, and Jeffrey Heer. 2011 · 2011
Earlier work this paper cites.
Building data science teams
DJ Patil. 2011 · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio. 2012 · 2012
Earlier work this paper cites.
Who’s got the data? Interdependencies in science and technology collaborations
Christine L Borgman, Jillian C Wallis, and Matthew S Mayernik. 2012 · 2012
Earlier work this paper cites.
Interactive dynamics for visual analysis
Jeffrey Heer and Ben Shneiderman. 2012 · 2012
Earlier work this paper cites.
Auto-WEKA: Automated Selection and Hyper-Parameter Optimization of Classification Algorithms
Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. 2012 · 2012
Earlier work this paper cites.
Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data
Urvesh Bhowan, Mark Johnston, Mengjie Zhang, and Xin Yao. 2013 · 2013
Earlier work this paper cites.
Evaluation of manually created ground truth for multi-view people localization. In Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications . ACM, 9
Ákos Kiss and Tamás Szirányi. 2013 · 2013
Earlier work this paper cites.
Data scientists aren’t domain experts
Stijn Viaene. 2013 · 2013
Earlier work this paper cites.
Easy hyperparameter search using optunity
Marc Claesen, Jaak Simm, Dusan Popovic, Yves Moreau, and Bart De Moor. 2014 · 2014
Earlier work this paper cites.
Data scientist: The engineer of the future
Wil MP Van der Aalst. 2014 · 2014
Earlier work this paper cites.
Efficient and robust automated machine learning. In Advances in Neural Information Processing Systems . 2962–2970
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015 · 2015
Earlier work this paper cites.
Mining administrative data to spur urban revitalization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1829–1838
Ben Green, Alejandra Caro, Matthew Conway, Robert Manduca, Tom Plagge, and Abby Miller. 2015 · 2015
Earlier work this paper cites.
Deep feature synthesis: Towards automating data science endeavors. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 1–10
James Max Kanter and Kalyan Veeramachaneni. 2015 · 2015
Earlier work this paper cites.
Principles of explanatory debugging to personalize interactive machine learning. In Proceedings of the 20th international conference on intelligent user interfaces . 126–137
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015 · 2015
Earlier work this paper cites.
The professionalisation of data science
Michael A Walker. 2015 · 2015
Earlier work this paper cites.
Developing a research agenda for human-centered data science. In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion . ACM, 529–535
Cecilia Aragon, Clayton Hutto, Andy Echenique, Brittany Fiore-Gartland, Yun Huang, Jinyoung Kim, Gina Neff, Wanli Xing, and Joseph Bayer. 2016 · 2016
Earlier work this paper cites.
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Andreas Holzinger. 2016 · 2016
Earlier work this paper cites.
ExploreKit: Automatic Feature Generation and Selection. In Proceedings of the IEEE 16th International Conference on Data Mining . 979–984
Gilad Katz, Eui Chul, Richard Shin, and Dawn Song. 2016 · 2016
Earlier work this paper cites.
Cognito: Automated feature engineering for supervised learning. In 2016 IEEE 16th International Conference on Data Mining . IEEE, 1304–1307
Udayan Khurana, Deepak Turaga, Horst Samulowitz, and Srinivasan Parthasrathy. 2016 · 2016
Earlier work this paper cites.
The emerging role of data scientists on software development teams. In Proceedings of the 38th International Conference on Software Engineering . ACM, 96–107
Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2016 · 2016
Earlier work this paper cites.
TPOT: A tree-based pipeline optimization tool for automating machine learning. In Workshop on Automatic Machine Learning . 66–74
Randal S Olson and Jason H Moore. 2016 · 2016
Earlier work this paper cites.
Selecting near-optimal learners via incremental data allocation. In Thirtieth AAAI Conference on Artificial Intelligence
Ashish Sabharwal, Horst Samulowitz, and Gerald Tesauro. 2016 · 2016
Cited alongside, same era.
Human-centered machine learning through interactive visualization. ESANN
Dominik Sacha, Michael Sedlmair, Leishi Zhang, John Aldo Lee, Daniel Weiskopf, Stephen North, and Daniel Keim. 2016 · 2016
Cited alongside, same era.
Big data and data science: what should we teach?
Il-Yeol Song and Yongjun Zhu. 2016 · 2016
Cited alongside, same era.
Conscientious classification: A data scientist’s guide to discrimination-aware classification
Brian d’Alessandro, Cathy O’Neil, and Tom LaGatta. 2017 · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
Incorporating domain knowledge in machine learning for soccer outcome prediction
Daniel Berrar, Philippe Lopes, and Werner Dubitzky. 2019 · 2019
Later among the works it cites.
Human-centered tools for coping with imperfect algorithms during medical decision-making. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, 4
Carrie J Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S Corrado, Martin C Stumpe, et al · 2019
Later among the works it cites.
Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso. 2019 · 2019
Later among the works it cites.
Training for Cross-Disciplinary Research and Science as a Team Sport
Jennifer L Clarke and Bob Wilhelm. 2019 · 2019
Later among the works it cites.
Automated Machine Learning for Predictive Modeling
DataRobot. [n.d.] · 2019
Later among the works it cites.
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A design perspective on data. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems . ACM, 2952–2963
Melanie Feinberg. 2017 · 2017
Cited alongside, same era.
Hacking with NPOs: collaborative analytics and broker roles in civic data hackathons
Youyang Hou and Dakuo Wang. 2017 · 2017
Cited alongside, same era.
Auto-WEKA 2.0: Automatic Model Selection and Hyperparameter Optimization in WEKA
Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, and Kevin Leyton-Brown. 2017 · 2017
Cited alongside, same era.
One button machine for automating feature engineering in relational databases
Hoang Thanh Lam, Johann-Michael Thiebaut, Mathieu Sinn, Bei Chen, Tiep Mai, and Oznur Alkan. 2017 · 2017
Cited alongside, same era.
Data vision: Learning to see through algorithmic abstraction. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing . ACM, 2436–2447
Samir Passi and Steven Jackson. 2017 · 2017
Cited alongside, same era.
Holoclean: Holistic data repairs with probabilistic inference
Theodoros Rekatsinas, Xu Chu, Ihab F Ilyas, and Christopher Ré. 2017 · 2017
Cited alongside, same era.
The 80/20 data science dilemma
Armand Ruiz and Armand Ruiz. 2017 · 2017
Cited alongside, same era.
Automated Feature Enhancement for Predictive Modeling using External Knowledge. In 2019 International Conference on Data Mining . IEEE, 1094–1097
Sainyam Galhotra, Udayan Khurana, Oktie Hassanzadeh, Kavitha Srinivas, Horst Samulowitz, and Miao Qi. 2019 · 2019
Later among the works it cites.
Towards human-guided machine learning. In Proceedings of the 24th International Conference on Intelligent User Interfaces . ACM, 614–624
Yolanda Gil, James Honaker, Shikhar Gupta, Yibo Ma, Vito D’Orazio, Daniel Garijo, Shruti Gadewar, Qifan Yang, and Neda Jahanshad. 2019 · 2019
Later among the works it cites.
Cloud AutoML
Google. [n.d.]a · 2019
Later among the works it cites.
Colaboratory
Google. [n.d.]b · 2019
Later among the works it cites.
The Semantic Snake Charmer Search Engine: A Tool to Facilitate Data Science in High-tech Industry Domains. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval . ACM, 355–359
Corrado Grappiolo, Emile van Gerwen, Jack Verhoosel, and Lou Somers. 2019 · 2019
Later among the works it cites.
Jupyter Notebook
Project Jupyter. [n.d.]a · 2019
Later among the works it cites.
Towards Effective Foraging by Data Scientists to Find Past Analysis Choices. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, 92
Mary Beth Kery, Bonnie E John, Patrick O’Flaherty, Amber Horvath, and Brad A Myers. 2019 · 2019
Later among the works it cites.
Will you accept an imperfect ai? exploring designs for adjusting end-user expectations of ai systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . 1–14
Rafal Kocielnik, Saleema Amershi, and Paul N Bennett. 2019 · 2019
Later among the works it cites.
Alphaclean: Automatic generation of data cleaning pipelines
Sanjay Krishnan and Eugene Wu. 2019 · 2019
Later among the works it cites.
Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, 263
Sean Kross and Philip J Guo. 2019 · 2019
Later among the works it cites.
A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead
Doris Jung-Lin Lee, Stephen Macke, Doris Xin, Angela Lee, Silu Huang, and Aditya Parameswaran. 2019 · 2019
Later among the works it cites.
Discovering the Sweet Spot of Human-Computer Configurations: A Case Study in Information Extraction
Maximilian Mackeprang, Claudia Müller-Birn, and Maximilian Timo Stauss. 2019 · 2019
Later among the works it cites.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
Later among the works it cites.
Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, 220–229
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Later among the works it cites.
Human-Centered Study of Data Science Work Practices. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, W15
Michael Muller, Melanie Feinberg, Timothy George, Steven J Jackson, Bonnie E John, Mary Beth Kery, and Samir Passi. 2019a · 2019
Later among the works it cites.
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, UK) (CHI ’19) . ACM, New York, NY, USA, Forthcoming
Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019b · 2019
Later among the works it cites.
Can we trust AutoML to go on full autopilot?
Norman Niemer, David Chen, Saad Naqvi, Zeana Kaynat, and Yongcheng Zhu. 2019 · 2019
Later among the works it cites.
The Automatic Statistician
Christian Steinruecken, Emma Smith, David Janz, James Robert Lloyd, and Zoubin Ghahramani. 2019 · 2019
Later among the works it cites.
How Data Scientists Use Computational Notebooks for Real-Time Collaboration. In Proceedings of the 2019 CHI Conference Extended Abstracts on Human Factors in Computing Systems . article 39
April Yi Wang, Anant Mittal, Christopher Brooks, and Steve Oney. 2019b · 2019
Later among the works it cites.
Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI
Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019c · 2019
Later among the works it cites.
Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, 601
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. 2019d · 2019
Later among the works it cites.
Atmseer: Increasing transparency and controllability in automated machine learning. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . ACM, 681
Qianwen Wang, Yao Ming, Zhihua Jin, Qiaomu Shen, Dongyu Liu, Micah J Smith, Kalyan Veeramachaneni, and Huamin Qu. 2019a · 2019
Later among the works it cites.
Survey on Automated Machine Learning
Marc-André Zöller and Marco F Huber. 2019 · 2019
Later among the works it cites.
Towards Automating the AI Operations Lifecycle
Matthew Arnold, Jeffrey Boston, Michael Desmond, Evelyn Duesterwald, Benjamin Elder, Anupama Murthi, Jiri Navratil, and Darrell Reimer. 2020 · 2020
Later among the works it cites.
Exploring Information Needs for Establishing Trust in Automated Data Science Systems
Jaimie Drozdal, Justin Weisz, Dakuo Wang, Dass Gaurave, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, and Hui Su. 2020 · 2020
Later among the works it cites.
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . 1–14
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
Later among the works it cites.
Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices. In Companion of the 2020 ACM International Conference on Supporting Group Work . 151–156
Marina Kogan, Aaron Halfaker, Shion Guha, Cecilia Aragon, Michael Muller, and Stuart Geiger. 2020 · 2020
Later among the works it cites.
Magic Quadrant for data science and machine-learning platforms
Peter Krensky, Pieter den Harner, Erick Brethenoux, Jim Hare, Svetlana Sicular, and Shubhangi Vashisth. 2020 · 2020
Later among the works it cites.
Questioning the AI: Informing Design Practices for Explainable AI User Experiences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . ACM
Q Vera Liao, Daniel Gruen, and Sarah Miller. 2020 · 2020
Later among the works it cites.
How Data Scientists Work Together With Domain Experts in Scientific Collaborations. In Proceedings of the 2020 ACM conference on GROUP . ACM
Yaoli Mao, Dakuo Wang, Michael Muller, Kush Varshney, Ioana Baldini, Casey Dugan, and Aleksandra Mojsilovic. 2020 · 2020
Later among the works it cites.
Human-centered artificial intelligence: Reliable, safe & trustworthy
Ben Shneiderman. 2020 · 2020
Later among the works it cites.
Callisto: Capturing the “Why” by Connecting Conversations with Computational Narratives. In Proceedings of the 2020 CHI Conference Extended Abstracts on Human Factors in Computing Systems . in press
April Yi Wang, Zihan Wu, Christopher Brooks, and Steve Oney. 2020c · 2020
Later among the works it cites.
From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems . 1–6
Dakuo Wang, Elizabeth Churchill, Pattie Maes, Xiangmin Fan, Ben Shneiderman, Yuanchun Shi, and Qianying Wang. 2020a · 2020
Later among the works it cites.
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates. In IUI’20 . ACM, in press
Daniel Weidele, Justin Weisz, Erick Oduor, Michael Muller, Josh Andres, Alexander Gray, and Dakuo Wang. 2020 · 2020
Later among the works it cites.
Bryan Wilder, Eric Horvitz, and Ece Kamar. 2020 · 2020
Later among the works it cites.
How do Data Science Workers Collaborate?: Roles,Workflows, and Tools. In CSCW’20 . ACM, in press
Amy Zhang, Michael Muller, and Dakuo Wang. 2020 · 2020
Later among the works it cites.