Learning Graphical Models
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Lingge Li, Dustin Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi
Neural Information Processing Systems http://nips.cc/
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
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/
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
Neural Information Processing Systems http://nips.cc/
MinimumSteinDiscrepancyEstimators
Examples of such models arise naturally in modelling images [27, 39], natural language [54], Markov random fields [61] and nonparametric density estimation [63, 69].
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.
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Marek Petrik, Reazul Hasan Russel
Neural Information Processing Systems http://nips.cc/
