130 citations · 178 across the 8 of their papers we have counts for
11 papers · 1 filter
A General Purpose Neural Architecture for Geospatial Systems
Nasim Rahaman, Martin Weiss, Frederik Träuble +6
Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. H…
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste +4
Learning the causal structure that underlies data is a crucial step towards robust real-world decision making. The majority of existing work in causal inference focuses on determin…
Can Active Learning Preemptively Mitigate Fairness Issues?
Frédéric Branchaud-Charron, Parmida Atighehchian, Pau Rodríguez +2
Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset preparation stages therefore becomes an essen…
Differentiable Causal Discovery from Interventional Data
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste +2
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line…
Bayesian active learning for production, a systematic study and a reusable library
Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs. While there are many papers on new active…
Stochastic Neural Network with Kronecker Flow
Chin-Wei Huang, Ahmed Touati, Pascal Vincent +3
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting o…