Kush R. Varshney

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Matrix factorization from a small number of observed entries has recently garnered much attention as the key ingredient of successful recommendation systems. One unresolved problem in this area is how to adapt current methods to handle changing user preferences over time. Recent proposals to address this issue are heuristic in nature and do not fully(More)
A variational level set method is developed for the supervised classification problem. Nonlinear classifier decision boundaries are obtained by minimizing an energy functional that is composed of an empirical risk term with a margin-based loss and a geometric regularization term new to machine learning: the surface area of the decision boundary. This(More)
We propose a new algorithm for estimation, prediction, and recommendation named the collaborative Kalman filter. Suited for use in collaborative filtering settings encountered in recommendation systems with significant temporal dynamics in user preferences, the approach extends probabilistic matrix factorization in time through a state-space model. This(More)
Sparse signal representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-structured, sparse signal representation algorithm for overcomplete dictionaries that can be decomposed into subdictionaries and whose dictionary elements can be arranged in a hierarchy.(More)
Low-dimensional statistics of measurements play an important role in detection problems, including those encountered in sensor networks. In this work, we focus on learning low-dimensional linear statistics of high-dimensional measurement data along with decision rules defined in the low-dimensional space in the case when the probability density of the(More)
Arthroplasty, the implantation of prostheses into joints, is a surgical procedure that is affecting a larger and larger number of patients over time. As a result, it is increasingly important to develop imaging techniques to noninvasively examine joints with prostheses after surgery, both statically and dynamically in 3-D. The static problem is considered(More)
In this paper, Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error (MBRE) as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained.(More)
Computational creativity and cognitive computing are distinct fields that have developed in a parallel fashion. In this paper, we examine the relationship between the two, concluding that the two fields overlap in one precise way: the evaluation or assessment of artifacts with respect to creativity. Furthermore, we discuss a particular instance of(More)
Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. Broadly, creativity involves a generative step to produce many ideas and a selective step to determine the ones that are the best. Many previous attempts at computational creativity, however, have not been able to achieve a(More)