Extreme Multi-label Learning via Nearest Neighbor Graph Partitioning and Embedding
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Active Positive Semidefinite Matrix Completion With Applications to Large Scale Bandit Problems
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Generalization Error Bounds for Extreme Multi-class Classification
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
A Parallel Primal-Dual Sparse Method for Extreme Classification
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Tight Learning Bounds for Multi-Class Classification
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Microsoft Research and Microsoft Azure improve the efficiency and capacity of cloud-scale optical networks
Today, at HotNets 2017, the Sixteenth ACM Workshop on Hot Topics in Networks, we shared with you some important research results from Microsoft Research and Microsoft Azure that show how we can increase the capacity of…