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Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain.
Model-based direct adjustment
Paul R Rosenbaum · 1987
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Identification of causal effects using instrumental variables
Joshua D Angrist, Guido W Imbens, and Donald B Rubin · 1996
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A kernel method for multi-labelled classification
André Elisseeff and Jason Weston · 2002
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Learning multi-label scene classification
Matthew R Boutell, Jiebo Luo, Xipeng Shen, and Christopher M Brown · 2004
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Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin · 2005
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How to get the most out of your curation effort
Andrey Rzhetsky, Hagit Shatkay, and W John Wilbur · 2009
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Active learning from crowds
Yan Yan, Romer Rosales, Glenn Fung, and Jennifer G Dy · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Combining crowd and expert labels using decision theoretic active learning
An Nguyen, Byron Wallace, and Matthew Lease · 2015
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Counterfactual risk minimization: Learning from logged bandit feedback
Adith Swaminathan and Thorsten Joachims · 2015
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The self-normalized estimator for counterfactual learning
Adith Swaminathan and Thorsten Joachims · 2015
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The power and limits of predictive approaches to observational-data-driven optimization
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A bayesian nonparametric approach for estimating individualized treatment-response curves
Yanbo Xu, Yanxun Xu, and Suchi Saria · 2016
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Deep learning
Yoshua Bengio, Ian Goodfellow, and Aaron Courville · 2017
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Cost-effective active learning from diverse labelers
Sheng-Jun Huang, Jia-Lve Chen, Xin Mu, and Zhi-Hua Zhou · 2017
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Detecting latent heterogeneity
Judea Pearl · 2017
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The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke · 2018
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The algorithmic automation problem: Prediction, triage, and human effort
Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan · 2019
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Algorithmic risk assessment in the hands of humans
Megan T Stevenson and Jennifer L Doleac · 2019
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Gagan Bansal, Besmira Nushi, Ece Kamar, Eric Horvitz, and Daniel S Weld · 2020
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A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores
Maria De-Arteaga, Riccardo Fogliato, and Alexandra Chouldechova · 2020
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Regression under human assistance
Abir De, Paramita Koley, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2020
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Learning under selective labels in the presence of expert consistency
Maria De-Arteaga, Artur Dubrawski, and Alexandra Chouldechova · 2018
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Decision making with limited feedback: Error bounds for predictive policing and recidivism prediction
Danielle Ensign, Sorelle A Friedler, Scott Nevlle, Carlos Scheidegger, and Suresh Venkatasubramanian · 2018
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Deep learning with logged bandit feedback
Thorsten Joachims, Adith Swaminathan, and Maarten de Rijke · 2018
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Multi-label learning from crowds
Shao-Yuan Li, Yuan Jiang, Nitesh V Chawla, and Zhi-Hua Zhou · 2018
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Predict responsibly: improving fairness and accuracy by learning to defer
David Madras, Toni Pitassi, and Richard Zemel · 2018
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Ben Green and Yiling Chen · 2020
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Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
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Model selection in contextual stochastic bandit problems
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, and Csaba Szepesvari · 2020
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Augmented fairness: An interpretable model augmenting decision-makers’ fairness
Tong Wang and Maytal Saar-Tsechansky · 2020
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Learning to complement humans
Bryan Wilder, Eric Horvitz, and Ece Kamar · 2020
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