Fetching the paper…
Reading the bibliography…
We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classification models.
On measures of dependence
A. Rényi · 1959
Earlier work this paper cites.
Incorporating invariances in support vector learning machines
B. Schölkopf, C. Burges, and V. Vapnik · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
Earlier work this paper cites.
Domain adaptation for statistical classifiers
H. Daume III and D. Marcu · 2006
Earlier work this paper cites.
A kernel statistical test of independence
A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf, and A. J. Smola · 2008
Earlier work this paper cites.
Covariate shift by kernel mean matching
A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf · 2009
Earlier work this paper cites.
Regression by dependence minimization and its application to causal inference in additive noise models
J. Mooij, D. Janzing, J. Peters, and B. Schölkopf · 2009
Earlier work this paper cites.
Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
Earlier work this paper cites.
Event labeling combining ensemble detectors and background knowledge
H. Fanaee-T and J. Gama · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Cited alongside, same era.
Data-analysis strategies for image-based cell profiling
J. C. Caicedo, S. Cooper, F. Heigwer, S. Warchal, P. Qiu, C. Molnar, A. S. Vasilevich, J. D. Barry, H. S. Bansal, O. Kraus, et al · 2017
Cited alongside, same era.
Conditional variance penalties and domain shift robustness
C. Heinze-Deml and N. Meinshausen · 2017
Cited alongside, same era.
Automated analysis of high-content microscopy data with deep learning
Foundations of machine learning
M. Mohri, A. Rostamizadeh, and A. Talwalkar · 2018
Later among the works it cites.
A. Rosenfeld, R. Zemel, and J. K. Tsotsos · 2018
Later among the works it cites.
Anchor regression: heterogeneous data meets causality
D. Rothenhäusler, N. Meinshausen, P. Bühlmann, and J. Peters · 2018
Later among the works it cites.
Counterfactual normalization: Proactively addressing dataset shift using causal mechanisms
A. Subbaswamy and S. Saria · 2018
Later among the works it cites.
M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
O. Z. Kraus, B. T. Grys, J. Ba, Y. Chong, B. J. Frey, C. Boone, and B. J. Andrews · 2017
Cited alongside, same era.
Variance-based regularization with convex objectives
H. Namkoong and J. C. Duchi · 2017
Cited alongside, same era.
Adversarial discriminative domain adaptation
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell · 2017
Cited alongside, same era.
Learning models with uniform performance via distributionally robust optimization
J. Duchi and H. Namkoong · 2018
Cited alongside, same era.
Causality from a distributional robustness point of view
N. Meinshausen · 2018
Cited alongside, same era.
Measuring statistical dependence with hilbert-schmidt norms
A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf
Cited in the paper.
Rademacher composition and linear prediction
A. T. Sham Kakade
Cited in the paper.
Adversarial feature augmentation for unsupervised domain adaptation
R. Volpi, P. Morerio, S. Savarese, and V. Murino
Cited in the paper.
The cells out of sample (coos) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
A. Lu, A. Lu, W. Schormann, M. Ghassemi, D. Andrews, and A. Moses · 2019
Closest in time.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
Closest in time.
Preventing failures due to dataset shift: Learning predictive models that transport
A. Subbaswamy, P. Schulam, and S. Saria · 2019
Closest in time.
Kernel methods for measuring independence
A. Gretton, R. Herbrich, A. Smola, O. Bousquet, and B. Schölkopf · 2075
Closest in time.