Bayesian Learning
Stochastic Expectation Maximization with Variance Reduction
However, sEM has a slow asymptotic convergence rate due to the high variance of each update.
Evidential Deep Learning to Quantify Classification Uncertainty
Murat Sensoy, Lance Kaplan, Melih Kandemir
Neural Information Processing Systems http://nips.cc/
Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
Computationally and statistically efficient learning of causal Bayes nets using path queries
Neural Information Processing Systems http://nips.cc/
Data-dependent PAC-Bayes priors via differential privacy
Gintare Karolina Dziugaite, Daniel M. Roy
Neural Information Processing Systems http://nips.cc/
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik
We introduce a principled approach for unsupervised structure learning of deep neural networks.
A Bayesian Approach to Generative Adversarial Imitation Learning
Wonseok Jeon, Seokin Seo, Kee-Eung Kim
Y et, one of the fundamental challenges lies in the fact that obtaining trajectory samples from the environment is often very costly, e.g., physical robots situated in real-world.
Implicit Reparameterization Gradients
Mikhail Figurnov, Shakir Mohamed, Andriy Mnih
However, it is not applicable to a number of important continuous distributions.
Transfer of Value Functions via Variational Methods Andrea Tirinzoni Politecnico di Milano nullnullnullnullnullnullnullnullnullnullnullnullnullnullnull
We consider the problem of transferring value functions in reinforcement learning.
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn, Kelvin Xu, Sergey Levine
In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution.