research
∙
06/30/2021
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
We revisit the theoretical properties of Hamiltonian stochastic differen...
share
research
∙
06/11/2021
Model Selection for Bayesian Autoencoders
We develop a novel method for carrying out model selection for Bayesian ...
research
∙
11/25/2020
All You Need is a Good Functional Prior for Bayesian Deep Learning
The Bayesian treatment of neural networks dictates that a prior distribu...
research
∙
11/10/2020
Sparse within Sparse Gaussian Processes using Neighbor Information
Approximations to Gaussian processes based on inducing variables, combin...
research
∙
06/09/2020
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
In this work we define a unified mathematical framework to deepen our un...
research
∙
06/08/2020
A Variational View on Bootstrap Ensembles as Bayesian Inference
In this paper, we employ variational arguments to establish a connection...
research
∙
07/01/2018
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters
Nowadays, data-centers are largely under-utilized because resource alloc...
research
∙
05/28/2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classi...
research
∙
11/06/2017
Probabilistic Model Checking for Continuous Time Markov Chains via Sequential Bayesian Inference
Probabilistic model checking for systems with large or unbounded state s...
research
∙
06/03/2016
