Multimodal large-scale pretraining has shown impressive performance gain...
Feature selection has been widely used to alleviate compute requirements...
Attention-based architectures have become ubiquitous in machine learning...
Hypergraphs provide a natural representation for many real world dataset...
We present simple differentially private estimators for the mean and
cov...
We investigate the problem of efficiently computing optimal transport (O...
We introduce COPT, a novel distance metric between graphs defined via an...
The Optimal Transport (a.k.a. Wasserstein) distance is an increasingly
p...
We study two problems in high-dimensional robust statistics: robust
mean...
We present new secure protocols for approximate k-nearest neighbor searc...
Most of the efficient sublinear-time indexing algorithms for the
high-di...
Space partitions of R^d underlie a vast and important class of
fast near...