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Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier.
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The foundations of cost-sensitive learning
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Cost-sensitive learning by cost-proportionate example weighting
Bianca Zadrozny, John Langford, and Naoki Abe · 2003
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Loss functions for binary class probability estimation and classification: Structure and applications
Andreas Buja, Werner Stuetzle, and Yi Shen · 2005
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Thresholding for Making Classifiers Cost-sensitive
Victor S. Sheng and Charles X. Ling · 2006
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Inconsistency of expert judgment-based estimates of software development effort
Stein Grimstad and Magne Jørgensen · 2007
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Learning multiple layers of features from tiny images
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Composite binary losses
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Extraneous factors in judicial decisions
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A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
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The minizinc challenge 2008–2013
Peter J Stuckey, Thibaut Feydy, Andreas Schutt, Guido Tack, and Julien Fischer · 2014
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Learning with Rejection
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 2016
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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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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks and Kevin Gimpel · 2017
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LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Optuna: A Next-generation Hyperparameter Optimization Framework
Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2022
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Sample Efficient Learning of Predictors that Complement Humans
Mohammad-Amin Charusaie, Hussein Mozannar, David A. Sontag, and Samira Samadi · 2022
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Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, and Gerhard Satzger · 2022
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Kori Inkpen, Shreya Chappidi, Keri Mallari, Besmira Nushi, Divya Ramesh, Pietro Michelucci, Vani Mandava, Libuše Hannah Vepřek, and Gabrielle Quinn · 2022
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Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
Sérgio Jesus, José Pombal, Duarte Alves, André F Cruz, Pedro Saleiro, Rita P Ribeiro, João Gama, and Pedro Bizarro · 2022
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Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Hybrid Intelligence
Dominik Dellermann, Philipp Ebel, Matthias Soellner, and Jan Marco Leimeister · 2019
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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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Artificial intelligence for anti-money laundering: a review and extension
Jingguang Han, Yuyun Huang, Sha Liu, and Kieran Towey · 2020
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Consistent Estimators for Learning to Defer to an Expert
Hussein Mozannar and David A. Sontag · 2020
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Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2021
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The accuracy, equity, and jurisprudence of criminal risk assessment
Sharad Goel, Ravi Shroff, Jennifer Skeem, and Christopher Slobogin · 2021
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Post-hoc estimators for learning to defer to an expert
Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya K Menon, Ankit Rawat, and Sanjiv Kumar · 2022
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
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Calibrated Learning to Defer with One-vs-All Classifiers
Rajeev Verma and Eric T. Nalisnick · 2022
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Mixture-of-experts with expert choice routing
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M Dai, Quoc V Le, James Laudon, et al · 2022
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Human–ai interactions in public sector decision making:“automation bias” and “selective adherence” to algorithmic advice
Saar Alon-Barkat and Madalina Busuioc · 2023
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Learning to defer with limited expert predictions
Patrick Hemmer, Lukas Thede, Michael Vössing, Johannes Jakubik, and Niklas Kühl · 2023
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Who should predict? exact algorithms for learning to defer to humans
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, and David Sontag · 2023
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Cp-sat, 2023
Laurent Perron and Frédéric Didier · 2023
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Leveraged calibrated loss for learning to defer
JM Steege · 2023
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Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles
Rajeev Verma, Daniel Barrejón, and Eric Nalisnick · 2023
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Two-stage learning to defer with multiple experts
Anqi Mao, Christopher Mohri, Mehryar Mohri, and Yutao Zhong · 2024
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