log likelihood
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
Neural Information Processing Systems http://nips.cc/
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Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
Further, we emphasize the significance of principled regularization of the network parameters and prediction.
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Proximal Graphical Event Models
Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
Neural Information Processing Systems http://nips.cc/
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Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas
Contributions: In this work, we develop a projected Stein variational Newton method (pSVN) to tackle the challenge of high-dimensional Bayesian inference by exploiting the intrinsic lowdimensional geometric structure of the posterior distribution (where it departs from the prior), as characterized by the dominant spectrum of the prior-preconditioned Hessian of the negative log likelihood.
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Supplementary Material for: Pareto Domain Adaptation
Do the main claims made in the abstract and introduction accurately reflect the paper's