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Expert decision makers are starting to rely on data-driven automated agents to assist them with various tasks.
The reduced nearest neighbor rule (corresp.)
Geoffrey Gates · 1972
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Learning from examples: Instructional principles from the worked examples research
Robert K Atkinson, Sharon J Derry, Alexander Renkl, and Donald Wortham · 2000
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Fast condensed nearest neighbor rule
Fabrizio Angiulli · 2005
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The power of feedback
John Hattie and Helen Timperley · 2007
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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An efficient sparse metric learning in high-dimensional space via l 1-penalized log-determinant regularization
Guo-Jun Qi, Jinhui Tang, Zheng-Jun Zha, Tat-Seng Chua, and Hong-Jiang Zhang · 2009
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Software Framework for Topic Modelling with Large Corpora
Radim Řehůřek and Petr Sojka · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Limits in decision making arise from limits in memory retrieval
Gyslain Giguère and Bradley C Love · 2013
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Submodular function maximization
Andreas Krause and Daniel Golovin · 2014
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Stochastic neighbor compression
Matt Kusner, Stephen Tyree, Kilian Weinberger, and Kunal Agrawal · 2014
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Visual category learning
Jennifer J Richler and Thomas J Palmeri · 2014
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Near-optimally teaching the crowd to classify
Adish Singla, Ilija Bogunovic, Gabor Bartok, Amin Karbasi, and Andreas Krause · 2014
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Learning the features used to decide how to teach
Min Hyung Lee, Joe Runde, Warfa Jibril, Zhuoying Wang, and Emma Brunskill · 2015
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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A human-is-the-loop approach for semi-automated content moderation
Daniel Link, Bernd Hellingrath, and Jie Ling · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Reminders of past choices bias decisions for reward in humans
Aaron M Bornstein, Mel W Khaw, Daphna Shohamy, and Nathaniel D Daw · 2017
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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer
Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017
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Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Protonn: Compressed and accurate knn for resource-scarce devices
Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, and Prateek Jain · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Interpretable machine teaching via feature feedback
Shihan Su, Yuxin Chen, Oisin Mac Aodha, Pietro Perona, and Yisong Yue · 2017
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Fast classification with binary prototypes
Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, and Inderjit Dhillon · 2017
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Near-optimal machine teaching via explanatory teaching sets
Yuxin Chen, Oisin Mac Aodha, Shihan Su, Pietro Perona, and Yisong Yue · 2018
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Do explanations make vqa models more predictable to a human?
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh · 2018
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W Taylor · 2018
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A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy
Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M Vardoulakis · 2020
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Proxy tasks and subjective measures can be misleading in evaluating explainable ai systems
Zana Bucinca, Phoebe Lin, Krzysztof Z Gajos, and Elena L Glassman · 2020
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Gagan Bansal, Tongshuang Wu, Joyce Zhu, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S Weld · 2020
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Are visual explanations useful? a case study in model-in-the-loop prediction
Eric Chu, Deb Roy, and Jacob Andreas · 2020
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Understanding the power and limitations of teaching with imperfect knowledge
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Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, and Adish Singla · 2018
Cited alongside, same era.
Predict responsibly: Improving fairness and accuracy by learning to defer
David Madras, Toni Pitassi, and Richard Zemel · 2018
Cited alongside, same era.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Cited alongside, same era.
An overview of machine teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles, and Anna N Rafferty · 2018
Cited alongside, same era.
Guidelines for human-ai interaction
Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al · 2019
Cited alongside, same era.
Beyond accuracy: The role of mental models in human-ai team performance
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz · 2019
Cited alongside, same era.
Cognitive model priors for predicting human decisions
David D Bourgin, Joshua C Peterson, Daniel Reichman, Stuart J Russell, and Thomas L Griffiths · 2019
Cited alongside, same era.
Rati Devidze, Farnam Mansouri, Luis Haug, Yuxin Chen, and Adish Singla · 2020
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Human evaluation of spoken vs. visual explanations for open-domain qa
Ana Valeria Gonzalez, Gagan Bansal, Angela Fan, Robin Jia, Yashar Mehdad, and Srinivasan Iyer · 2020
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Evaluating explainable ai: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal · 2020
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Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan · 2020
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" why is’ chicago’deceptive?" towards building model-driven tutorials for humans
Vivian Lai, Han Liu, and Chenhao Tan · 2020
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Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
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Supporting children’s math learning with feedback-augmented narrative technology
Sherry Ruan, Jiayu He, Rui Ying, Jonathan Burkle, Dunia Hakim, Anna Wang, Yufeng Yin, Lily Zhou, Qianyao Xu, Abdallah AbuHashem, et al · 2020
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Misplaced trust: Measuring the interference of machine learning in human decision-making
Harini Suresh, Natalie Lao, and Ilaria Liccardi · 2020
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No explainability without accountability: An empirical study of explanations and feedback in interactive ml
Alison Smith-Renner, Ron Fan, Melissa Birchfield, Tongshuang Wu, Jordan Boyd-Graber, Daniel S Weld, and Leah Findlater · 2020
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Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou · 2020
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Human–computer collaboration for skin cancer recognition
Philipp Tschandl, Christoph Rinner, Zoe Apalla, Giuseppe Argenziano, Noel Codella, Allan Halpern, Monika Janda, Aimilios Lallas, Caterina Longo, Josep Malvehy, et al · 2020
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A general model validation and testing tool
Kevin Vanslette, Tony Tohme, and Kamal Youcef-Toumi · 2020
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Bryan Wilder, Eric Horvitz, and Ece Kamar · 2020
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Chexplain: Enabling physicians to explore and understand data-driven, ai-enabled medical imaging analysis
Yao Xie, Melody Chen, David Kao, Ge Gao, and Xiang’Anthony’ Chen · 2020
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Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making
Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy · 2020
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Is the most accurate ai the best teammate? optimizing ai for teamwork
Gagan Bansal, Besmira Nushi, Ece Kamar, Eric Horvitz, and Daniel S Weld · 2021
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Do as ai say: susceptibility in deployment of clinical decision-aids
Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J Berkowitz, Eva Lermer, Joseph F Coughlin, John V Guttag, Errol Colak, and Marzyeh Ghassemi · 2021
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The teaching dimension of kernel perceptron
Akash Kumar, Hanqi Zhang, Adish Singla, and Yuxin Chen · 2021
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Assessing the impact of automated suggestions on decision making: Domain experts mediate model errors but take less initiative
Ariel Levy, Monica Agrawal, Arvind Satyanarayan, and David Sontag · 2021
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Harini Suresh, Kathleen M Lewis, John V Guttag, and Arvind Satyanarayan · 2021
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Improving regression uncertainty estimation under statistical change
Tony Tohme, Kevin Vanslette, and Kamal Youcef-Toumi · 2021
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A human-centered agenda for intelligible machine learning
Jennifer Wortman Vaughan and Hanna Wallach · 2021
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