Research

Distribution Matching

Distribution matching has the opposite objective of classification and is useful for constraining ML problems.

Localized Learning

Localized learning avoids end-to-end learning by updating model parts via non-global objectives.

Explainable AI

Explaining distribution shifts and prediction uncertainty are important for modern AI systems.

Selected Publications

Distribution alignment can be used to learn invariant representations with applications in fairness and robustness. Most prior works …

A distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly …

Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are …

A central theme in federated learning (FL) is the fact that client data distributions are often not independent and identically …

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned …

While normalizing flows for continuous data have been extensively researched, flows for discrete data have only recently been explored. …

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing …

The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair …

Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on …

While previous distribution shift detection approaches can identify if a shift has occurred, these approaches cannot localize which …

In practical applications of machine learning, it is necessary to look beyond standard metrics such as test accuracy in order to …

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 …

An introduction to artificial intelligence (AI).

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

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

This course introduces Python programming to students through data science problems. Students learn Python concepts as well as …

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

This course introduces Python programming to students through data science problems. Students learn Python concepts as well as …

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

Contact