Fetching the paper…
Reading the bibliography…
We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source and a target domain.
Comparison of a screening test and a reference test in epidemiologic studies. ii. a probabilistic model for the comparison of diagnostic tests
AA Buck, JJ Gart, et al · 1966
Earlier work this paper cites.
On tail probabilities for martingales
David A Freedman · 1975
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir Naumovich Vapnik · 1999
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
Marco Saerens, Patrice Latinne, and Christine Decaestecker · 2002
Earlier work this paper cites.
Discriminant analysis and statistical pattern recognition , volume 544
Geoffrey McLachlan · 2004
Earlier work this paper cites.
Mcdiarmid’s inequalities of bernstein and bennett forms
Yiming Ying · 2004
Earlier work this paper cites.
Learning and evaluating classifiers under sample selection bias
Bianca Zadrozny · 2004
Earlier work this paper cites.
Word sense disambiguation with distribution estimation
Yee Seng Chan and Hwee Tou Ng · 2005
Earlier work this paper cites.
Correcting sample selection bias by unlabeled data
Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola · 2007
Earlier work this paper cites.
Learning from multiple sources
Koby Crammer, Michael Kearns, and Jennifer Wortman · 2008
Earlier work this paper cites.
Quantifying counts and costs via classification
George Forman · 2008
Earlier work this paper cites.
Importance weighted active learning
Alina Beygelzimer, Sanjoy Dasgupta, and John Langford · 2009
Earlier work this paper cites.
Covariate shift by kernel mean matching
Arthur Gretton, Alexander J Smola, Jiayuan Huang, Marcel Schmittfull, Karsten M Borgwardt, and Bernhard Schölkopf · 2009
Earlier work this paper cites.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Cited alongside, same era.
When training and test sets are different: characterizing learning transfer
Amos Storkey · 2009
Cited alongside, same era.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Cited alongside, same era.
Semi-supervised novelty detection
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2010
Cited alongside, same era.
Learning bounds for importance weighting
Corinna Cortes, Yishay Mansour, and Mehryar Mohri · 2010
Cited alongside, same era.
Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
Later among the works it cites.
Domain adaptation and sample bias correction theory and algorithm for regression
Corinna Cortes and Mehryar Mohri · 2014
Later among the works it cites.
Maximum mean discrepancy for class ratio estimation: Convergence bounds and kernel selection
Arun Iyer, Saketha Nath, and Sunita Sarawagi · 2014
Later among the works it cites.
Robust classification under sample selection bias
Anqi Liu and Brian Ziebart · 2014
Later among the works it cites.
Class proportion estimation with application to multiclass anomaly rejection
Tyler Sanderson and Clayton Scott · 2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Cited alongside, same era.
A method of moments for mixture models and hidden markov models
Animashree Anandkumar, Daniel Hsu, and Sham M Kakade · 2012
Cited alongside, same era.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Cited alongside, same era.
A spectral algorithm for learning hidden markov models
Daniel Hsu, Sham M Kakade, and Tong Zhang · 2012
Cited alongside, same era.
Statistical linear estimation with penalized estimators: an application to reinforcement learning
Bernardo Avila Pires and Csaba Szepesvári · 2012
Cited alongside, same era.
On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
Cited alongside, same era.
Kamyar Azizzadenesheli, Alessandro Lazaric, and Animashree Anandkumar · 2016
Later among the works it cites.
Robust covariate shift regression
Xiangli Chen, Mathew Monfort, Anqi Liu, and Brian D Ziebart · 2016
Later among the works it cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
Later among the works it cites.
Mixture proportion estimation via kernel embeddings of distributions
Harish Ramaswamy, Clayton Scott, and Ambuj Tewari · 2016
Later among the works it cites.
Active learning for cost-sensitive classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daume III, and John Langford · 2017
Later among the works it cites.
Trimmed density ratio estimation
Song Liu, Akiko Takeda, Taiji Suzuki, and Kenji Fukumizu · 2017
Later among the works it cites.
Reconciling modern machine learning and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2018
Later among the works it cites.
Detecting and correcting for label shift with black box predictors
Zachary C Lipton, Yu-Xiang Wang, and Alex Smola · 2018
Later among the works it cites.
High-dimensional statistics: A non-asymptotic viewpoint
M. J. Wainwright · 2019
Closest in time.