Big Learning with Bayesian Methods
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceBIG-bench Machine LearningDistributed ComputingSimilar Papers 제목 키워드 기반
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review
Bayesian inference has many advantages in decision making of agents (e.g. robotics/simulative agent) over a regular data-driven black-box neural network: Data-efficiency, generalization, interpretability, and safety wher…
Active LearningBayesian InferenceBayesian OptimizationDecision Making+4Bayesian Reinforcement Learning: A Survey
Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of…
Bayesian Inferencereinforcement-learningReinforcement LearningReinforcement Learning (RL)+1A Tutorial on Learning With Bayesian Networks
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data anal…
Do Bayesian imaging methods report trustworthy probabilities?
Bayesian statistics is a cornerstone of imaging sciences, underpinning many and varied approaches from Markov random fields to score-based denoising diffusion models. In addition to powerful image estimation methods, the…
DenoisingGPUUncertainty QuantificationRoBO: A Flexible and Robust Bayesian Optimization Framework in Python
Bayesian optimization is a powerful approach for the global derivative-free optimization of non-convex expensive functions. Even though there is a rich literature on Bayesian optimization, the source code of advanced met…
Bayesian OptimizationHyperparameter Optimization