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Found 8 papers, 3 papers with code
Stochastic Approximation with Biased MCMC for Expectation Maximization
In practice, MCMC-SAEM is often run with asymptotically biased MCMC, for which the consequences are theoretically less understood.
Provably Scalable Black-Box Variational Inference with Structured Variational Families
In fact, recent computational complexity results for BBVI have established that full-rank variational families scale poorly with the dimensionality of the problem compared to e. g. mean field families.
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification.
On the Convergence of Black-Box Variational Inference
We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference.
The Behavior and Convergence of Local Bayesian Optimization
A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to traditional global strategies.
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements.
Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound
Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals.
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior.
