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Out-of-domain (OOD) generalization is a significant challenge for machine learning models.
Verification of forecasts expressed in terms of probability
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Causality
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Adam: A method for stochastic optimization
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Identity mappings in deep residual networks
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Causal inference by using invariant prediction: identification and confidence intervals
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Deep coral: Correlation alignment for deep domain adaptation
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On calibration of modern neural networks
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Densely connected convolutional networks
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Attention is all you need
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
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Functional map of the world
G. Christie, N. Fendley, J. Wilson, and R. Mukherjee · 2018
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Multicalibration: Calibration for the (computationally-identifiable) masses
U. Hébert-Johnson, M. Kim, O. Reingold, and G. Rothblum · 2018
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Invariant causal prediction for nonlinear models
C. Heinze-Deml, J. Peters, and N. Meinshausen · 2018
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Does distributionally robust supervised learning give robust classifiers?
Preventing failures due to dataset shift: Learning predictive models that transport
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Evaluating model calibration in classification
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Generalization and invariances in the presence of unobserved confounding
A. Bellot and M. van der Schaar · 2020
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Calibration of pre-trained transformers
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In search of lost domain generalization
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W. Hu, G. Niu, I. Sato, and M. Sugiyama · 2018
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Accurate uncertainties for deep learning using calibrated regression
V. Kuleshov, N. Fenner, and S. Ermon · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
A. Kumar, S. Sarawagi, and U. Jain · 2018
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Domain adaptation by using causal inference to predict invariant conditional distributions
S. Magliacane, T. van Ommen, T. Claassen, S. Bongers, P. Versteeg, and J. M. Mooij · 2018
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Anchor regression: heterogeneous data meets causality
D. Rothenhäusler, N. Meinshausen, P. Bühlmann, and J. Peters · 2018
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J. R. Zech, M. A. Badgeley, M. Liu, A. B. Costa, J. J. Titano, and E. K. Oermann · 2018
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M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
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C. Gupta, A. Podkopaev, and A. Ramdas · 2020
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Wilds: A benchmark of in-the-wild distribution shifts
P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, S. Beery, et al · 2020
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Out-of-distribution generalization via risk extrapolation (rex)
D. Krueger, E. Caballero, J.-H. Jacobsen, A. Zhang, J. Binas, R. L. Priol, and A. Courville · 2020
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Calibrating deep neural networks using focal loss
J. Mukhoti, V. Kulharia, A. Sanyal, S. Golodetz, P. H. Torr, and P. K. Dokania · 2020
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Intra order-preserving functions for calibration of multi-class neural networks
A. Rahimi, A. Shaban, C.-A. Cheng, R. Hartley, and B. Boots · 2020
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The risks of invariant risk minimization
E. Rosenfeld, P. Ravikumar, and A. Risteski · 2020
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Improved protein structure prediction using potentials from deep learning
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Zídek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis · 2020
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Sample complexity of uniform convergence for multicalibration
E. Shabat, L. Cohen, and Y. Mansour · 2020
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Transferable calibration with lower bias and variance in domain adaptation
X. Wang, M. Long, J. Wang, and M. I. Jordan · 2020
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Using publicly available satellite imagery and deep learning to understand economic well-being in africa
C. Yeh, A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke · 2020
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Out-of-distribution prediction with invariant risk minimization: The limitation and an effective fix
R. Guo, P. Zhang, H. Liu, and E. Kiciman · 2021
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Does invariant risk minimization capture invariance?
P. Kamath, A. Tangella, D. J. Sutherland, and N. Srebro · 2021
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