We study the problem of learning a hierarchical tree representation of d...
We give the first polynomial time algorithms for escaping from
high-dime...
Stochastic gradient descent (SGD) is a prevalent optimization technique ...
We initiate a comprehensive experimental study of objective-based
hierar...
Hierarchical Clustering is an unsupervised data analysis method which ha...
A function f : F_2^n →R is s-sparse if it has at
most s non-zero Fourier...
The problem of selecting a small-size representative summary of a large
...
We study the problem of constructing a linear sketch of minimum dimensio...
We propose the first adversarially robust algorithm for monotone submodu...
Motivated by performance optimization of large-scale graph processing sy...
Recent works on Hierarchical Clustering (HC), a well-studied problem in
...
We study the relation between streaming algorithms and linear sketching
...
We present massively parallel (MPC) algorithms and hardness of approxima...