Felix X. Yu

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Google ∙ Columbia University

Federated Learning with Only Positive Labels

We consider learning a multi-class classification model in the federated...
04/21/2020 ∙ by Felix X. Yu, et al. ∙ 9 ∙ share

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Pre-training Tasks for Embedding-based Large-scale Retrieval

We consider the large-scale query-document retrieval problem: given a qu...
02/10/2020 ∙ by Wei-Cheng Chang, et al. ∙ 4 ∙ share

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Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...
12/10/2019 ∙ by Peter Kairouz, et al. ∙ 33 ∙ share

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AdaCliP: Adaptive Clipping for Private SGD

Privacy preserving machine learning algorithms are crucial for learning ...
08/20/2019 ∙ by Venkatadheeraj Pichapati, et al. ∙ 1 ∙ share

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The Sparse Recovery Autoencoder

Linear encoding of sparse vectors is widely popular, but is most commonl...
06/26/2018 ∙ by Shanshan Wu, et al. ∙ 6 ∙ share

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Orthogonal Random Features

We present an intriguing discovery related to Random Fourier Features: i...
10/28/2016 ∙ by Felix X. Yu, et al. ∙ 0 ∙ share

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Compact Nonlinear Maps and Circulant Extensions

Kernel approximation via nonlinear random feature maps is widely used in...
03/12/2015 ∙ by Felix X. Yu, et al. ∙ 0 ∙ share

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An exploration of parameter redundancy in deep networks with circulant projections

We explore the redundancy of parameters in deep neural networks by repla...
02/11/2015 ∙ by Yu Cheng, et al. ∙ 0 ∙ share

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Circulant Binary Embedding

Binary embedding of high-dimensional data requires long codes to preserv...
05/13/2014 ∙ by Felix X. Yu, et al. ∙ 0 ∙ share

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On Learning from Label Proportions

Learning from Label Proportions (LLP) is a learning setting, where the t...
02/24/2014 ∙ by Felix X. Yu, et al. ∙ 0 ∙ share

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∝SVM for learning with label proportions

We study the problem of learning with label proportions in which the tra...
06/04/2013 ∙ by Felix X. Yu, et al. ∙ 0 ∙ share

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Felix X. Yu

Featured Co-authors

Yang Liu

Mehdi Bennis

Yu Cheng

Dawn Song

Shih-Fu Chang

Qiang Yang

Martin Jaggi

Ramesh Raskar

Dong Liu

Sanjiv Kumar

Alexandros G. Dimakis

Federated Learning with Only Positive Labels

Pre-training Tasks for Embedding-based Large-scale Retrieval

Advances and Open Problems in Federated Learning

AdaCliP: Adaptive Clipping for Private SGD

The Sparse Recovery Autoencoder

Orthogonal Random Features

Compact Nonlinear Maps and Circulant Extensions

An exploration of parameter redundancy in deep networks with circulant projections

Circulant Binary Embedding

On Learning from Label Proportions

∝SVM for learning with label proportions