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stat.ML2019
Efficient Approximate Inference with Walsh-Hadamard Variational Inference
Simone Rossi, Sebastien Marmin, Maurizio Filippone
Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes.…
stat.ML2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Simone Rossi, Sebastien Marmin, Maurizio Filippone
Over-parameterized models, such as DeepNets and ConvNets, form a class of models that are routinely adopted in a wide variety of applications, and for which Bayesian inference is d…
stat.ML2018
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…