Scalable and Reliable Inference for Probabilistic Modeling
Explore scalable and reliable inference techniques for probabilistic modeling in this 42-minute lecture by Ruqi Zhang from UT Austin. Delve into the Metropolis-Hastings algorithm and learn how minibatching can enhance its scalability. Examine Poisson-Minibatching's guaranteed exactness and scalability through empirical verification on Gaussian mixtures and logistic regression. Investigate Gibbs sampling for graphical models and its Poisson-Minibatching adaptation. Discover why deep learning requires reliable inference and explore stochastic gradient MCMC improvements. Analyze the Cyclical Stepsize Schedule solution for efficient exploration, including convergence guarantees. Apply these concepts to mixture models, Bayesian neural networks, and ImageNet. Gain insights into pushing the frontier of efficient inference for reliable deep learning in this Joint IFML/CCSI Symposium talk from the Simons Institute.