Quantile Formulation for Optimization under a Qualitative Risk Constraint
This paper focuses on the pathologies of common gradient-based algorithms for solving optimization problems under probability constraints. These problems are...
This paper focuses on the pathologies of common gradient-based algorithms for solving optimization problems under probability constraints. These problems are...
Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults. This pape...
Matrix multiplication is a fundamental building block in various distributed computing algorithms. In order to multiply large matrices, it is common practice...
We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and di...
Tensors, i.e., multi-linear functions, are a fundamental building block of machine learning algorithms. In order to train on large data-sets, it is common pr...