Fetching the paper…
Reading the bibliography…
A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies.
The comparison and evaluation of forecasters
DeGroot, M. H. and Fienberg, S. E · 1983
Earlier work this paper cites.
Are humans good intuitive statisticians after all? rethinking some conclusions from the literature on judgment under uncertainty
Cosmides, L. and Tooby, J · 1996
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. et al · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Zadrozny, B. and Elkan, C · 2001
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B. and Elkan, C · 2002
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
Discriminative learning for differing training and test distributions
Bickel, S., Brückner, M., and Scheffer, T · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Calibration of machine learning models
Bella, A., Ferri, C., Hernández-Orallo, J., and Ramírez-Quintana, M. J · 2010
Earlier work this paper cites.
Learning bounds for importance weighting
Cortes, C., Mansour, Y., and Mohri, M · 2010
Cited alongside, same era.
Adapting visual category models to new domains
Saenko, K., Kulis, B., Fritz, M., and Darrell, T · 2010
Cited alongside, same era.
Machine learning forecasts of risk to inform sentencing decisions
Berk, R. and Hyatt, J · 2015
Cited alongside, same era.
Nearest neighbor density ratio estimation for large-scale applications in astronomy
Kremer, J., Gieseke, F., Pedersen, K. S., and Igel, C · 2015
Cited alongside, same era.
Optimized assistive human–robot interaction using reinforcement learning
Modares, H., Ranatunga, I., Lewis, F. L., and Popa, D. O · 2015
Cited alongside, same era.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Deep hashing network for unsupervised domain adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S · 2017
Later among the works it cites.
A survey of domain adaptation for neural machine translation
Chu, C. and Wang, R · 2018
Later among the works it cites.
Conditional adversarial domain adaptation
Long, M., Cao, Z., Wang, J., and Jordan, M. I · 2018
Later among the works it cites.
Bias correction of learned generative models using likelihood-free importance weighting
Grover, A., Song, J., Kapoor, A., Tran, K., Agarwal, A., Horvitz, E. J., and Ermon, S · 2019
Later among the works it cites.
A review of domain adaptation without target labels
Kouw, W. M. and Loog, M · 2019
Later among the works it cites.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Deep learning for finance: deep portfolios
Heaton, J., Polson, N., and Witte, J. H · 2017
Cited alongside, same era.
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J., Ren, J., and Nado, Z · 2019
Later among the works it cites.
Applications of machine learning in real-life digital health interventions: Review of the literature
Triantafyllidis, A. K. and Tsanas, A · 2019
Later among the works it cites.
Towards accurate model selection in deep unsupervised domain adaptation
You, K., Wang, X., Long, M., and Jordan, M · 2019
Later among the works it cites.