Fetching the paper…
Reading the bibliography…
We present a study of surrogate losses and algorithms for the general problem of learning to defer with multiple experts.
Application of the logistic function to bio-assay
Joseph Berkson · 1944
Earlier work this paper cites.
Why I prefer logits to probits
Joseph Berkson · 1951
Earlier work this paper cites.
An optimum character recognition system using decision function
C.K. Chow · 1957
Earlier work this paper cites.
On optimum recognition error and reject tradeoff
C Chow · 1970
Earlier work this paper cites.
Multi-class support vector machines
Jason Weston and Chris Watkins · 1998
Earlier work this paper cites.
Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data
Yoonkyung Lee, Yi Lin, and Grace Wahba · 2004
Earlier work this paper cites.
Statistical behavior and consistency of classification methods based on convex risk minimization
Tong Zhang · 2004
Earlier work this paper cites.
Classification with reject option
Radu Herbei and Marten Wegkamp · 2005
Earlier work this paper cites.
Convexity, classification, and risk bounds
Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe · 2006
Earlier work this paper cites.
How to compare different loss functions and their risks
Ingo Steinwart · 2007
Earlier work this paper cites.
Classification with a reject option using a hinge loss
Peter L Bartlett and Marten H Wegkamp · 2008
Earlier work this paper cites.
Support vector machines with a reject option
Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, and Stéphane Canu · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
On the foundations of noise-free selective classification
Ran El-Yaniv et al · 2010
Earlier work this paper cites.
Classification methods with reject option based on convex risk minimization
Ming Yuan and Marten Wegkamp · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Agnostic selective classification
Yair Wiener and Ran El-Yaniv · 2011
Earlier work this paper cites.
SVMs with a reject option
Ming Yuan and Marten Wegkamp · 2011
Earlier work this paper cites.
Reliable agnostic learning
Adam Tauman Kalai, Varun Kanade, and Yishay Mansour · 2012
Earlier work this paper cites.
Combining human and machine intelligence in large-scale crowdsourcing
Ece Kamar, Severin Hacker, and Eric Horvitz · 2012
Earlier work this paper cites.
Consistency versus realizable H-consistency for multiclass classification
Phil Long and Rocco Servedio · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Multi-class deep boosting
Vitaly Kuznetsov, Mehryar Mohri, and Umar Syed · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
Earlier work this paper cites.
Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2018
Earlier work this paper cites.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
Cited alongside, same era.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Cited alongside, same era.
Consistent algorithms for multiclass classification with an abstain option
Harish G Ramaswamy, Ambuj Tewari, and Shivani Agarwal · 2018
Cited alongside, same era.
Investigating human+ machine complementarity for recidivism predictions
Sarah Tan, Julius Adebayo, Kori Inkpen, and Ece Kamar · 2018
Cited alongside, same era.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
Cited alongside, same era.
Directing human attention in event localization for clinical timeline creation
Jason Zhao, Monica Agrawal, Pedram Razavi, and David Sontag · 2021
Later among the works it cites.
Counterfactual inference of second opinions
Nina L Corvelo Benz and Manuel Gomez Rodriguez · 2022
Later among the works it cites.
Generalizing consistent multi-class classification with rejection to be compatible with arbitrary losses
Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie Gu, Bo An, Gang Niu, and Masashi Sugiyama · 2022
Later among the works it cites.
Sample efficient learning of predictors that complement humans
Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag, and Samira Samadi · 2022
Later among the works it cites.
Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, and Gerhard Satzger · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Selectivenet: A deep neural network with an integrated reject option
Yonatan Geifman and Ran El-Yaniv · 2019
Cited alongside, same era.
On the calibration of multiclass classification with rejection
Chenri Ni, Nontawat Charoenphakdee, Junya Honda, and Masashi Sugiyama · 2019
Cited alongside, same era.
The algorithmic automation problem: Prediction, triage, and human effort
Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan · 2019
Cited alongside, same era.
Deep gamblers: Learning to abstain with portfolio theory
Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2019
Cited alongside, same era.
Budget learning via bracketing
Durmus Alp Emre Acar, Aditya Gangrade, and Venkatesh Saligrama · 2020
Cited alongside, same era.
Regression under human assistance
Abir De, Paramita Koley, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2020
Cited alongside, same era.
Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
Cited alongside, same era.
Incorporating uncertainty in learning to defer algorithms for safe computer-aided diagnosis
Jessie Liu, Blanca Gallego, and Sebastiano Barbieri · 2022
Later among the works it cites.
Teaching humans when to defer to a classifier via exemplars
Hussein Mozannar, Arvind Satyanarayan, and David Sontag · 2022
Later among the works it cites.
Post-hoc estimators for learning to defer to an expert
Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, and Sanjiv Kumar · 2022
Later among the works it cites.
Provably improving expert predictions with conformal prediction
Eleni Straitouri, Lequn Wang, Nastaran Okati, and Manuel Gomez Rodriguez · 2022
Later among the works it cites.
Calibrated learning to defer with one-vs-all classifiers
Rajeev Verma and Eric Nalisnick · 2022
Later among the works it cites.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
Later among the works it cites.
Theoretically grounded loss functions and algorithms for adversarial robustness
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong · 2023
Closest in time.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Closest in time.
In defense of softmax parametrization for calibrated and consistent learning to defer
Yuzhou Cao, Hussein Mozannar, Lei Feng, Hongxin Wei, and Bo An · 2023
Closest in time.
Regression with cost-based rejection
Xin Cheng, Yuzhou Cao, Haobo Wang, Hongxin Wei, Bo An, and Lei Feng · 2023
Closest in time.
Theory and algorithms for learning with rejection in binary classification
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2023
Closest in time.
Learning to defer with limited expert predictions
Patrick Hemmer, Lukas Thede, Michael Vössing, Johannes Jakubik, and Niklas Kühl · 2023
Closest in time.
Ranking with abstention
Anqi Mao, Mehryar Mohri, and Yutao Zhong · 2023
Closest in time.
Learning to reject meets ood detection: Are all abstentions created equal?
Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, and Sanjiv Kumar · 2023
Closest in time.
Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles
Rajeev Verma, Daniel Barrejón, and Eric Nalisnick · 2023
Closest in time.
Revisiting discriminative vs. generative classifiers: Theory and implications
Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, and Jun Zhu · 2023
Closest in time.
DC-programming for neural network optimizations
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, and Yutao Zhong · 2024
Closest in time.
Learning to make adherence-aware advice
Guanting Chen, Xiaocheng Li, Chunlin Sun, and Hanzhao Wang · 2024
Closest in time.
When no-rejection learning is optimal for regression with rejection
Xiaocheng Li, Shang Liu, Chunlin Sun, and Hanzhao Wang · 2024
Closest in time.
Learning to reject with a fixed predictor: Application to decontextualization
Christopher Mohri, Daniel Andor, Eunsol Choi, Michael Collins, Anqi Mao, and Yutao Zhong · 2024
Closest in time.