Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing
attention to the potential impacts of AI technologies on society. In this paper we discuss one …

This important text and reference for researchers and students in machine learning, game
theory, statistics and information theory offers a comprehensive treatment of the problem of …

Convex programming involves a convex set F⊆ Rn and a convex cost function c: F→ R. The
goal of convex programming is to find a point in F which minimizes c. In online convex …

Kernel-based algorithms such as support vector machines have achieved considerable
success in various problems in batch setting, where all of the training data is available in …

We consider the problem of clustering data over time. An evolutionary clustering should
simultaneously optimize two potentially conflicting criteria: first, the clustering at any point in …

High-velocity streams of high-dimensional data pose significant “big data” analysis
challenges across a range of applications and settings. Online learning and online convex …

We investigate online convex optimization in non-stationary environments and choose the
dynamic regret as the performance measure, defined as the difference between cumulative …

We investigate online convex optimization in non-stationary environments and choose
dynamic regret as the performance measure, defined as the difference between cumulative …

In this paper, we propose a novel machine learning framework called “Online Transfer
Learning”(OTL), which aims to attack an online learning task on a target domain by …