
-
Federated Learning with Only Positive Labels
We consider learning a multi-class classification model in the federated...
read it
-
Pre-training Tasks for Embedding-based Large-scale Retrieval
We consider the large-scale query-document retrieval problem: given a qu...
read it
-
Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
read it
-
AdaCliP: Adaptive Clipping for Private SGD
Privacy preserving machine learning algorithms are crucial for learning ...
read it
-
The Sparse Recovery Autoencoder
Linear encoding of sparse vectors is widely popular, but is most commonl...
read it
-
Orthogonal Random Features
We present an intriguing discovery related to Random Fourier Features: i...
read it
-
Compact Nonlinear Maps and Circulant Extensions
Kernel approximation via nonlinear random feature maps is widely used in...
read it
-
An exploration of parameter redundancy in deep networks with circulant projections
We explore the redundancy of parameters in deep neural networks by repla...
read it
-
Circulant Binary Embedding
Binary embedding of high-dimensional data requires long codes to preserv...
read it
-
On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the t...
read it
-
∝SVM for learning with label proportions
We study the problem of learning with label proportions in which the tra...
read it