Bilevel optimization, the problem of minimizing a value function which involves the arg-
minimum of another function, appears in many areas of machine learning. In a large scale …

We consider stochastic bilevel optimization problems involving minimizing an upper-level
($\texttt {UL} $) function that is dependent on the arg-min of a strongly-convex lower-level …

We propose a new fast algorithm to estimate any sparse generalized linear model with
convex or non-convex separable penalties. Our algorithm is able to solve problems with …

Empirical risk minimization (ERM) is a crucial framework that offers a general approach to
handling a broad range of machine learning tasks. In this paper, we propose a novel …

Training Deep Learning (DL) models require large, high-quality datasets, often assembled
with data from different institutions. Federated Learning (FL) has been emerging as a …

Reproducibility is the cornerstone of science. Many scientific communities have been struck
by the reproducibility crisis, and computer science is no exception. Its answer has been to …

We discuss guidelines for evaluating the performance of parameterized stochastic solvers
for optimization problems, with particular attention to systems that employ novel hardware …

Deep kernel machines (DKMs) are a recently introduced kernel method with the flexibility of
other deep models including deep NNs and deep Gaussian processes. DKMs work purely …

Bilevel optimization problems, which are problems where two optimization problems are
nested, have more and more applications in machine learning. In many practical cases, the …

We consider an important problem in scientific discovery, namely identifying sparse
governing equations for nonlinear dynamical systems. This involves solving sparse ridge …