18 citations · 65 across the 9 of their papers we have counts for
10 papers
Warped geometric information on the optimisation of Euclidean functions
Marcelo Hartmann, Bernardo Williams, Hanlin Yu +3
We consider the fundamental task of optimising a real-valued function defined in a potentially high-dimensional Euclidean space, such as the loss function in many machine-learning…
Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
Hanlin Yu, Marcelo Hartmann, Bernardo Williams +1
Stochastic-gradient sampling methods are often used to perform Bayesian inference on neural networks. It has been observed that the methods in which notions of differential geometr…
Lagrangian Manifold Monte Carlo on Monge Patches
Marcelo Hartmann, Mark Girolami, Arto Klami
The efficiency of Markov Chain Monte Carlo (MCMC) depends on how the underlying geometry of the problem is taken into account. For distributions with strongly varying curvature, Ri…
Prior knowledge elicitation: The past, present, and future
Petrus Mikkola, Osvaldo A. Martin, Suyog Chandramouli +9
Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. I…
Flexible Prior Elicitation via the Prior Predictive Distribution
Marcelo Hartmann, Georgi Agiashvili, Paul Bürkner +1
The prior distribution for the unknown model parameters plays a crucial role in the process of statistical inference based on Bayesian methods. However, specifying suitable priors…
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
Eliezer de Souza da Silva, Tomasz Kuśmierczyk, Marcelo Hartmann +1
The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters that are typically selecte…
Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data
Jarno Vanhatalo, Marcelo Hartmann, Lari Veneranta
Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description f…
Laplace approximation and the natural gradient for Gaussian process regression with the heteroscedastic Student-t model
Marcelo Hartmann, Jarno Vanhatalo
This paper considers the Laplace method to derive approximate inference for the Gaussian process (GP) regression in the location and scale parameters of the Student-t probabilistic…
Extending Owen's integral table and a new multivariate Bernoulli distribution
Marcelo Hartmann
We extend some equatilites in the Owen's table of normal integrals (A table of normal integrals in Communication in Statistics-Simulation and Computation, 1980). Furthermore a new…
Bayesian Inference for Generalized Extreme Value Distributions via Hamiltonian Monte Carlo
Marcelo Hartmann, Ricardo Ehlers
In this paper we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian a…