High-Dimensional Statistics
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 […]
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, […]
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 […]
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, […]
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. […]
FDS Colloquium: Song Mei (Berkeley), “Revisiting neural network approximation theory in the age of generative AI”
Optional Zoom link: https://yale.zoom.us/j/97222935172 Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it […]
