Minimax optimal convergence rates for numerous classes of stochastic convex optimization
problems are well characterized, where the majority of results utilize iterate averaged …

In this paper, we tackle air quality forecasting by using machine learning approaches to
predict the hourly concentration of air pollutants (eg, ozone, particle matter (PM 2.5) and …

In this paper, we study the efficiency of a Restarted SubGradient (RSG) method that
periodically restarts the standard subgradient method (SG). We show that, when applied to a …

In this paper, we consider statistical learning with AUC (area under ROC curve)
maximization in the classical stochastic setting where one random data drawn from an …

As data gets more complex and applications of machine learning (ML) algorithms for
decision-making broaden and diversify, traditional ML methods by minimizing an …

Stagewise training strategy is widely used for learning neural networks, which runs a
stochastic algorithm (eg, SGD) starting with a relatively large step size (aka learning rate) …

We develop algorithms for private stochastic convex optimization that adapt to the hardness
of the specific function we wish to optimize. While previous work provide worst-case bounds …

Although stochastic gradient descent (SGD) method and its variants (eg, stochastic
momentum methods, AdaGrad) are the choice of algorithms for solving non-convex …

Alternating direction method of multipliers (ADMM) has received tremendous interest for
solving numerous problems in machine learning, statistics and signal processing. However …

In this paper, we propose a practical online method for solving a class of distributional robust
optimization (DRO) with non-convex objectives, which has important applications in …