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Found 4 papers, 0 papers with code
Adaptive Bayesian Sampling with Monte Carlo EM
Our approach provides a simpler alternative, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and learning the mass matrices in the M step with a novel online technique.
Accelerated Stochastic Quasi-Newton Optimization on Riemann Manifolds
We propose an L-BFGS optimization algorithm on Riemannian manifolds using minibatched stochastic variance reduction techniques for fast convergence with constant step sizes, without resorting to linesearch methods designed to satisfy Wolfe conditions.
Gamma Processes, Stick-Breaking, and Variational Inference
In this paper, we present a variational inference framework for models involving gamma process priors.
Small-Variance Asymptotics for Hidden Markov Models
Starting with the standard HMM, we first derive a “hard” inference algorithm analogous to k-means that arises when particular variances in the model tend to zero.
