Optimization problems involving uncertainties are common in a variety of engineering disciplines such as transportation systems, manufacturing, communication networks, healthcare and finance. The large number of input ...

In recent years, deep neural network models have shown to outperform many state of the art algorithms. The reason for this is, unsupervised pretraining with multi-layered deep neural networks have shown to learn better ...

Profiling techniques to detect performance bugs in applications are usually customized to detect a specific bug pattern and involve significant engineering effort. In spite of this effort, many techniques either suffer from ...

Understanding the structure and complexity of a polynomial family is a fundamental problem of arithmetic circuit complexity. There are various approaches like studying the lower bounds, which deals with nding the smallest ...

Obtaining an accurate whole program path (WPP) that captures a program’s runtime behaviour in terms of a control-flow trace has a number of well-known benefits, including opportunities for code optimization, bug detection, ...

Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come ...

Recognition of text from camera-captured scene/born-digital images help in the development of aids for the blind, unmanned navigation systems and spam filters. However, text in such images is not confined to any page layout, ...

A fundamental question in a sequential decision making setting under uncertainty is “how to allocate resources amongst competing entities so as to maximize the rewards accumulated in the long run?”. The resources allocated ...

We consider supervised learning problems, where one is given objects with labels, and the goal is to learn a model that can make accurate predictions on new objects. These problems abound in applications, ranging from ...

Over the past decade, the explosive growth of the Internet has led to a surge of interest to understand and predict aggregate behavior of large number of people or agents, particularly when they are connected through an ...