24 citations · 37 across the 5 of their papers we have counts for
6 papers
Kernel computations from large-scale random features obtained by Optical Processing Units
Ruben Ohana, Jonas Wacker, Jonathan Dong +4
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation. However, performing random projection…
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.…
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…
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…
Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors
Sébastien Marmin, Clément Chevalier, David Ginsbourger
We deal with the efficient parallelization of Bayesian global optimization algorithms, and more specifically of those based on the expected improvement criterion and its variants.…
Differentiating the multipoint Expected Improvement for optimal batch design
Sébastien Marmin, Clément Chevalier, David Ginsbourger
This work deals with parallel optimization of expensive objective functions which are modeled as sample realizations of Gaussian processes. The study is formalized as a Bayesian op…