2 code implementations • 3 Feb 2020 • Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven
However, the performance of the variational approach depends on the choice of an appropriate variational family.
no code implementations • 29 Jan 2020 • Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-Draa, Marcel van Gerven, Francois Laviolette
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.
1 code implementation • 15 Nov 2019 • Max Hinne, David Leeftink, Marcel A. J. van Gerven, Luca Ambrogioni
Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions.
no code implementations • NeurIPS 2018 • Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.
no code implementations • 29 May 2018 • Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss.
no code implementations • NeurIPS 2017 • Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris
Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.