Newsroom
High-Dimensional Statistics
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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, […]
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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 […]
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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, […]
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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 […]
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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, […]
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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. […]