
Federated Learning with Only Positive Labels
We consider learning a multiclass classification model in the federated...
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Pretraining Tasks for Embeddingbased Largescale Retrieval
We consider the largescale querydocument retrieval problem: given a qu...
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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AdaCliP: Adaptive Clipping for Private SGD
Privacy preserving machine learning algorithms are crucial for learning ...
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The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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Orthogonal Random Features
We present an intriguing discovery related to Random Fourier Features: i...
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Compact Nonlinear Maps and Circulant Extensions
Kernel approximation via nonlinear random feature maps is widely used in...
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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...
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Circulant Binary Embedding
Binary embedding of highdimensional data requires long codes to preserv...
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On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the t...
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∝SVM for learning with label proportions
We study the problem of learning with label proportions in which the tra...
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Felix X. Yu
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