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Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox.
The mnist database of handwritten digits
Yann LeCun · 1998
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Sequential monte carlo methods for dynamic systems
Jun S Liu and Rong Chen · 1998
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Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan · 2007
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Direct importance estimation with model selection and its application to covariate shift adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul Von Buenau, and Motoaki Kawanabe · 2007
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Direct importance estimation for covariate shift adaptation
Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul von Bünau, and Motoaki Kawanabe · 2008
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Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation , pages 443–454
Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, and Masashi Sugiyama · 2008
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Covariate shift by kernel mean matching
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A least-squares approach to direct importance estimation
Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama · 2009
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Relative novelty detection
Alex Smola, Le Song, and Choon Hui Teo · 2009
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Direct importance estimation with gaussian mixture models
Makoto Yamada and Masashi Sugiyama · 2009
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Learning bounds for importance weighting
Corinna Cortes, Yishay Mansour, and Mehryar Mohri · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Nearest neighbor-based importance weighting
Marco Loog · 2012
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Constructive setting of the density ratio estimation problem and its rigorous solution
Vladimir Vapnik, Igor Braga, and Rauf Izmailov · 2013
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Relative density-ratio estimation for robust distribution comparison
Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, and Masashi Sugiyama · 2013
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Human-level concept learning through probabilistic program induction
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Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
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Mutual information neural estimation
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i-revnet: Deep invertible networks
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Glow: Generative flow with invertible 1x1 convolutions
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Invertible residual networks
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Danilo Rezende and Shakir Mohamed · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Improving variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
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Linking losses for density ratio and class-probability estimation
Aditya Menon and Cheng Soon Ong · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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A rad approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, and Hugo Larochelle · 2019
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Bias correction of learned generative models using likelihood-free importance weighting
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
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Hybrid models with deep and invertible features
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Normalizing flows for probabilistic modeling and inference
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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Understanding the limitations of variational mutual information estimators
Jiaming Song and Stefano Ermon · 2019
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Mintnet: Building invertible neural networks with masked convolutions
Yang Song, Chenlin Meng, and Stefano Ermon · 2019
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Learning likelihoods with conditional normalizing flows
Christina Winkler, Daniel Worrall, Emiel Hoogeboom, and Max Welling · 2019
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Towards accurate model selection in deep unsupervised domain adaptation
Kaichao You, Ximei Wang, Mingsheng Long, and Michael Jordan · 2019
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Fair generative modeling via weak supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, and Stefano Ermon · 2020
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Telescoping density-ratio estimation
Benjamin Rhodes, Kai Xu, and Michael U Gutmann · 2020
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Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U Gutmann, et al · 2021
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