Robust estimation is much more challenging in high-dimensions than it is in one-dimension:
Most techniques either lead to intractable optimization problems or estimators that can …

Approximation algorithms have developed in response to the impossibility of solving a great
variety of important optimization problems. Too frequently, when attempting to get a solution …

The Support Vector Machine is a powerful new learning algorithm for solving a variety of
learning and function estimation problems, such as pattern recognition, regression …

We prove optimal, up to an arbitrary ε> 0, inapproximability results for Max-E k-Sat for k≥ 3,
maximizing the number of satisfied linear equations in an over-determined system of linear …

N COMPUTER applications we are used to live with approximation. Var I ious notions of
approximation appear, in fact, in many circumstances. One notable example is the type of …

This paper deals with the computational complexity of conditions which guarantee that the
NP-hard problem of finding the sparsest solution to an underdetermined linear system can …

We investigate the computational complexity of two closely related classes of combinatorial
optimization problems for linear systems which arise in various fields such as machine …

In advanced service oriented systems, complex applications, described as abstract business
processes, can be executed by invoking a number of available Web services. End users can …

We prove the following about the Nearest Lattice Vector Problem (in anylpnorm), the
Nearest Codeword Problem for binary codes, the problem of learning a halfspace in the …

In statistical learning and analysis from shared data, which is increasingly widely adopted in
platforms such as federated learning and meta-learning, there are two major concerns …