841 citations · 1.2k across the 34 of their papers we have counts for
6 papers · 1 filter
Neural Likelihoods via Cumulative Distribution Functions
Pawel Chilinski, Ricardo Silva
We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs). By the applicatio…
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolo Colombo, Ricardo Silva
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance w…
Causal Interventions for Fairness
Matt J. Kusner, Chris Russell, Joshua R. Loftus +1
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness. While this may help pr…
Alpha-Beta Divergence For Variational Inference
Jean-Baptiste Regli, Ricardo Silva
This paper introduces a variational approximation framework using direct optimization of what is known as the {\it scale invariant Alpha-Beta divergence} (sAB divergence). This new…
Causal Reasoning for Algorithmic Fairness
Joshua R. Loftus, Chris Russell, Matt J. Kusner +1
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in…
Two-way sparsity for time-varying networks, with applications in genomics
Thomas E. Bartlett, Ioannis Kosmidis, Ricardo Silva
We propose a novel way of modelling time-varying networks, by inducing two-way sparsity on local models of node connectivity. This two-way sparsity separately promotes sparsity acr…