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1603.02501
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Mixture Proportion Estimation via Kernel Embedding of Distributions
8 March 2016
H. G. Ramaswamy
Clayton Scott
Ambuj Tewari
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Papers citing
"Mixture Proportion Estimation via Kernel Embedding of Distributions"
50 / 97 papers shown
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Konrad Furmañczyk
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318
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Label-Noise Robust Diffusion Models
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Yeongmin Kim
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Takashi Ishida
Yu Zhang
Gang Niu
Masashi Sugiyama
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Wenhai Wan
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Songcan Chen
361
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Paul Grigas
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Yilun Zhu
A. Fjeldsted
Darren C. Holland
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197
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Takayuki Katsuki
Takayuki Osogami
265
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Christophe Gerard Lambert
263
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Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
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Qinliang Su
Jianxing Yu
221
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202
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Qianqian Xu
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180
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Jian Zhang
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172
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Konrad Furmañczyk
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Wojciech Rejchel
Paweł Teisseyre
247
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Learning from Positive and Unlabeled Data with Augmented Classes
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Liutao Yang
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153
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Domain Adaptation under Open Set Label Shift
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Sivaraman Balakrishnan
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Adapting to Online Label Shift with Provable Guarantees
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424
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ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022
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Xiahai Zhuang
249
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Positive Unlabeled Contrastive Learning
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Sujay Sanghavi
Li Jing
Bhargav Bhushanam
Dhruv Choudhary
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Inderjit Dhillon
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A Boosting Algorithm for Positive-Unlabeled Learning
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Mingzhe Zhang
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Tony Chen
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218
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Risk bounds for PU learning under Selected At Random assumption
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P. Massart
P. Pamphile
210
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Mixture Proportion Estimation and PU Learning: A Modern Approach
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Yifan Wu
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Zachary Chase Lipton
226
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To Smooth or Not? When Label Smoothing Meets Noisy Labels
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Self-paced Resistance Learning against Overfitting on Noisy Labels
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Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift
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