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Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation.
On measures of information and entropy
Alfréd Rényi · 1960
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Least squares quantization in PCM
Stuart P Lloyd · 1982
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John C. Platt · 1999
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Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
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Obtaining well calibrated probabilities using Bayesian binning
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Concrete problems in ai safety
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Deep residual learning for image recognition
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Domain adaptation for visual applications: A comprehensive survey
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Model-agnostic meta-learning for fast adaptation of deep networks
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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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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I. Jordan · 2017
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Learning from simulated and unsupervised images through adversarial training
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Deep hashing network for unsupervised domain adaptation
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Starcraft II: A new challenge for reinforcement learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, and Rodney Tsing · 2017
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Metareg: Towards domain generalization using meta-regularization
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Transferrable prototypical networks for unsupervised domain adaptation
Yingwei Pan, Ting Yao, Yehao Li, Yu Wang, Chong-Wah Ngo, and Tao Mei · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
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Unsupervised domain adaptation through self-supervision
Yu Sun, Eric Tzeng, Trevor Darrell, and Alexei A. Efros · 2019
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Fixing the train-test resolution discrepancy
Hugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jégou · 2019
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Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation
Amr Alexandari, Anshul Kundaje, and Avanti Shrikumarn · 2020
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Technologies for trustworthy machine learning: A survey in a socio-technical context
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Transferable calibration with lower bias and variance in domain adaptation
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A survey of unsupervised deep domain adaptation
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Self-training with noisy student improves imagenet classification
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