Probabilistic methods in additive number theory exploit randomness to reveal typical structural features of sums and differences of integers. Originating in pioneering work on random sumsets, these ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

Previous high-order solvers are unstable for guided sampling: Samples use the pre-trained DPMs on ImageNet 256 256 with a classifier guidance scale 8.0, varying different samplers (and different ...

Data science and machine learning algorithms can help us form probabilistic forecasts of things like sporting events.

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 ...

Research of the probability and statistics group includes particle systems, theoretical statistics, non-conventional random walks, random matrix theory, and random polynomials. Research interests also ...