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- Centre National de la Recherche ScientifiqueFR14 papers
- Institut national de recherche en sciences et technologies du numériqueFR14 papers
- National Institute of InformaticsJP13 papers
- SupélecFR13 papers
- University of Illinois ChicagoUS10 papers
- Technical University of MunichDE9 papers
- Laboratoire Informatique d'AvignonFR8 papers
- University College LondonGB8 papers
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- Sorbonne UniversitéFR7 papers
- Université Côte d'AzurFR7 papers
- University of Eastern FinlandFI7 papers
5 papers · 2 filters
Variational Calibration of Computer Models
Sébastien Marmin, Maurizio Filippone
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involve…
Good Initializations of Variational Bayes for Deep Models
Simone Rossi, Pietro Michiardi, Maurizio Filippone
Stochastic variational inference is an established way to carry out approximate Bayesian inference for deep models. While there have been effective proposals for good initializatio…
Calibrating Deep Convolutional Gaussian Processes
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham +2
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the abili…
Counterfactual Mean Embeddings
Krikamol Muandet, Motonobu Kanagawa, Sorawit Saengkyongam +1
Counterfactual inference has become a ubiquitous tool in online advertisement, recommendation systems, medical diagnosis, and econometrics. Accurate modeling of outcome distributio…
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi, Maurizio Filippone
We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodolog…