Existing work on differentially private linear regression typically assu...
Compact user representations (such as embeddings) form the backbone of
p...
We propose a new definition of instance optimality for differentially pr...
We consider the problem of Learning from Label Proportions (LLP), a weak...
A reconstruction attack on a private dataset D takes as input some publi...
When applying differential privacy to sensitive data, a common way of ge...
This paper introduces the first provably accurate algorithms for
differe...
We consider a variation on the classical finance problem of optimal port...
We show a hardness result for random smoothing to achieve certified
adve...
Algorithms for scientific analysis typically have tunable parameters tha...
In an online optimization problem we are required to choose a sequence o...
Clustering is an important part of many modern data analysis pipelines,
...
In this work, we study the problem of online optimization of piecewise
L...
In classic fair division problems such as cake cutting and rent division...
Algorithms for clustering points in metric spaces is a long-studied area...
Tree search algorithms, such as branch-and-bound, are the most widely us...
In distributed machine learning, data is dispatched to multiple machines...