The scale of modern datasets necessitates the development of efficient distributed
optimization methods for machine learning. We present a general-purpose framework for …

Many convex problems in machine learning and computer science share the same
form:\begin {align*}\min_ {x}\sum_ {i} f_i (A_i x+ b_i),\end {align*} where $ f_i $ are convex …

For distributed computing environment, we consider the empirical risk minimization problem
and propose a distributed and communication-efficient Newton-type optimization method. At …

Successive quadratic approximations, or second-order proximal methods, are useful for
minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part …

Due to the rapid growth of data and computational resources, distributed optimization has
become an active research area in recent years. While first-order methods seem to dominate …

As the training dataset size and the model size of machine learning increase rapidly, more
computing resources are consumed to speedup the training process. However, the …

Many convex optimization problems with important applications in machine learning are
formulated as empirical risk minimization (ERM). There are several examples: linear and …

Despite the importance of sparsity in many large-scale applications, there are few methods
for distributed optimization of sparsity-inducing objectives. In this paper, we present a …

In recent years, there is a growing need to train machine learning models on a huge volume
of data. Therefore, designing efficient distributed optimization algorithms for empirical risk …

This work proposes a progressive manifold identification approach for distributed
optimization with sound theoretical justifications to greatly reduce both the rounds of …