Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory
Bayesian deep learning is a promising approach towards improved uncertainty quantification and sample efficiency. Due to their complex parameter space, choosing informative priors for Bayesian Neural Networks (BNNs) is challenging. Thus, often a naive, zero-centered Gaussian is used, resulting both in bad generalization and poor uncertainty estimates when training data is scarce. In contrast, meta-learning aims to extract such prior knowledge from a set of related learning tasks. We propose a principled and scalable algorithm for meta-learning BNN priors based on PAC-Bayesian bounds. Whereas previous approaches require optimizing the prior and multiple variational posteriors in an interdependent manner, our method does not rely on difficult nested optimization problems and is agnostic to the variational inference method in use. Our experiments show that the proposed method is not only computationally more efficient but also yields better predictions and uncertainty estimates when compared to previous meta-learning methods and BNNs with standard priors.
Code (0)
등록된 구현이 없습니다.
Tasks
Meta-LearningUncertainty QuantificationVariational InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Amortising Inference and Meta-Learning Priors in Neural Networks
One of the core facets of Bayesianism is in the updating of prior beliefs in light of new evidence$\text{ -- }$so how can we maintain a Bayesian approach if we have no prior beliefs in the first place? This is one of the…
Bayesian decision-making under misspecified priors with applications to meta-learning
Thompson sampling and other Bayesian sequential decision-making algorithms are among the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The choice of prior in these algorithms offer…
Decision MakingMeta-LearningMulti-Armed BanditsSequential Decision Making+1Meta-Learning Conjugate Priors for Few-Shot Bayesian Optimization
Bayesian Optimization is methodology used in statistical modelling that utilizes a Gaussian process prior distribution to iteratively update a posterior distribution towards the true distribution of the data. Finding unb…
Bayesian OptimizationMeta-LearningPriors Matter: Addressing Misspecification in Bayesian Deep Q-Learning
Uncertainty quantification in reinforcement learning can greatly improve exploration and robustness. Approximate Bayesian approaches have recently been popularized to quantify uncertainty in model-free algorithms. Howeve…
Reinforcement LearningMeta-Learning Priors for Safe Bayesian Optimization
In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in suc…
Bayesian OptimizationMeta-LearningUncertainty Quantification