Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In t ...

Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variabili ...

Worst-case analysis is often meaningless in practice. Some problems never reach the anticipated worst-case complexity. Other solutions get bogged down with impractical constants during implementation, despite having favo ...

Motivated by a real-world problem, we study a novel setting for budgeted optimization where the goal is to optimize an unknown function f(x) given a budget. In our setting, it is not practical to request samples of f(x) ...

Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although info ...

Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a ...

Recently, delta-sigma modulation has become a widely applied technique for high-performance analog-to-digital conversion of narrow-band signals. Most of the early designs used discrete-time structure for good accuracy an ...