Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that ...

Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a ...

The race to build bigger AI models is giving way to a more urgent contest over where and how those models actually run. Nvidia's multibillion dollar move on Groq has crystallized a shift that has been ...

It has been estimated that about 30% of the genes in the human genome are regulated by microRNAs (miRNAs). These are short RNA sequences that can down-regulate the levels of mRNAs or proteins in ...

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

Probabilistic graphical models are a powerful technique for handling uncertainty in machine learning. The course will cover how probability distributions can be represented in graphical models, how ...

Probabilistic inference depends exponentially on the so called tree width, which is a measure of the worst-case intermediate result during inference that is bounded from below by the maximum number of ...