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01/28/2022
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
In classical federated learning, the clients contribute to the overall t...
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05/12/2021
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number...
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04/21/2020
Federated Learning with Only Positive Labels
We consider learning a multi-class classification model in the federated...
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02/10/2020
Pre-training Tasks for Embedding-based Large-scale Retrieval
We consider the large-scale query-document retrieval problem: given a qu...
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12/10/2019
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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08/20/2019
AdaCliP: Adaptive Clipping for Private SGD
Privacy preserving machine learning algorithms are crucial for learning ...
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06/26/2018
The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
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10/28/2016
Orthogonal Random Features
We present an intriguing discovery related to Random Fourier Features: i...
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03/12/2015
Compact Nonlinear Maps and Circulant Extensions
Kernel approximation via nonlinear random feature maps is widely used in...
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02/11/2015
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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05/13/2014
Circulant Binary Embedding
Binary embedding of high-dimensional data requires long codes to preserv...
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02/24/2014
On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the t...
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06/04/2013