Selected Publications

We consider objective evaluation measures of explanations of complex black-box machine learning models. We propose simple robust …

We propose a unified framework for deep density models by formally defining density destructors. A density destructor is an invertible …

Existing research on label noise often focuses on simple uniform or classconditional noise. However, in many real-world settings, label …

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of …

Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of …

We propose a novel distribution that generalizes the Multinomial distribution to enable dependencies between dimensions. Our novel …

We develop a fast algorithm for the Admixture of Poisson MRFs (APM) topic model and propose a novel metric to directly evaluate this …

This paper introduces a new topic model based on an admixture of Poisson Markov Random Fields (APM), which can model dependencies …

Other Publications

Owing to the sheer volume of text generated by a microblog site like Twitter, it is often difficult to fully understand what is being …

Noise cancellation in an MRI environment is difficult due to the high noise levels that are in the spectral range of human speech. This …

Due to the sheer volume of text generated by a micro log site like Twitter, it is often difficult to fully understand what is being …

Teaching

A graduate-level project-based introduction to artificial intelligence (AI) with a primary focus on unsupervised learning. The lecture …

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