Publications

Recent Preprints and Papers

Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many …

Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and …

Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with n×n kernel matrices. To address the …

Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, …

The neural tangent kernel (NTK) has garnered significant attention as a theoretical framework for describing the behavior of …

Talks

Recent and Upcoming

Projects

Software, Tech Reports, etc.

Probabilistic Numerics (PN) interprets classic numerical routines as inference procedures by taking a probabilistic viewpoint. This …

In this project we analyzed time-optimal control problems with linear dynamics and numerical methods for solving them. When …

Contact

  • Columbia University
    Zuckerman Institute
    3227 Broadway
    New York, NY 10027
  • Office hours by appointment