Newsroom

High-Dimensional Statistics

  • Cancelled: FDS Colloquium: J. Nathan Kutz (UW), “Learning and Data Assimilation of Physics from Videos”

    This event has been cancelled. Talk summary: Sensing is a universal task in science and engineering. Downstream tasks from sensing include learning dynamical models, inferring full state estimates of a system (system identification), control decisions, and forecasting. These tasks are exceptionally challenging to achieve with limited sensors, noisy measurements, and corrupt or missing data. Existing…

  • FDS x Applied Physics Colloquium: Grant Rotskoff (Stanford), “Efficient variational inference with generative models”

    Abstract: Neural networks continue to surprise us with their remarkable capabilities for high-dimensional function approximation. Applications of machine learning now pervade essentially every scientific discipline, but predictive models to describe the optimization dynamics, inference properties, and flexibility of modern neural networks remain limited. In this talk, I will introduce several approaches to both analyzing and…

  • FDS Colloquium: Brice Huang (MIT), “Algorithmic thresholds in random optimization problems”

    Abstract: Optimizing high-dimensional functions generated from random data is a central problem in modern statistics and machine learning. As these objectives are highly non-convex, the maximum value reachable by efficient algorithms is usually smaller than the maximum value that exists, and characterizing the fundamental computational limits of these problems is a difficult challenge. In this…

  • S&DS Seminar: Jelena Bradic (UCSD), “Dynamic causal inference under model misspecification”

    Abstract: Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges due to the “curse of dimensionality” and time-varying confounding, which can lead to biased estimates. Additionally, correctly specifying the growing number of treatment assignments and outcome models with multiple exposures…

  • S&DS Seminar: Florentina Bunea (Cornell), “Learning Large Softmax Mixtures with Warm Start EM”

    Mixed multinomial logits are discrete mixtures introduced several decades ago to model the probability of choosing an attribute xj 2 RL from p possible candidates, in heteroge-neous populations. The model has recently attracted attention in the AI literature, under the name softmax mixtures, where it is routinely used in the nal layer of a neural network to map…

  • S&DS Seminar: Adityanand Guntuboyina (Berkeley), “Multivariate nonparametric regression using mixed partial derivatives”

    Information and Abstract: I will describe methods for multivariate nonparametric estimation based on constraining mixed partial derivatives. The resulting estimators are efficiently computable and work well in practice. They are also theoretically attractive when the underlying assumptions are satisfied, achieving rates of convergence that avoid the usual curse of dimensionality. Specific examples of this methodology include: MARS via LASSO,…

View more