DR MAURIZIO FILIPPONE
Photo
Associate Professor with the Statistics Program at KAUST

Address:
4700 King Abdullah University Of Science And Technology
Thuwal 23955-6900,
Kingdom of Saudi Arabia

Office: 1-4112

Phone: +966 12 808 7216

Email Address: maurizio.filippone [at] kaust.edu.sa



**** A message to perspective Post-Docs and PhD candidates ****

I'm always looking for talented students and Post-Docs who are passionate about Computational Statistics and Probabilistic Machine Learning. If you are interested in joining my group at KAUST, please get in touch!



NEWS

10-08-24  The paper "Improved Random Features for Dot Product Kernels" has been accepted for publication in JMLR!

08-08-24  Invited talk at the session on Advances in Inference and Theory for Bayesian Neural Networks at JSM in Portland, OR "Functional Priors for Bayesian Deep Learning" (link)

10-06-24  Invited talk at the Approximate Inference in Theory and Practice workshop in Paris: "Improving Optimization of Likelihood-based Generative Models with One Line of Code" (link)

02-05-24  The position paper entitled "Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI" has been accepted at ICML 2024! (link)

11-04-24  The paper "Spatial Bayesian neural networks" has been accepted in the Spatial Statistics journal! (link)

18-02-24  I moved to KAUST as an Associate Professor of Statistics!

21-09-23  The paper "One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models" has been accepted at NeurIPS 2023! (link)

21-09-23  The paper "Continuous-Time Functional Diffusion Processes" has been accepted at NeurIPS 2023! (link)

20-09-23  Delighted to be an invited speaker at the GenU workshop in Copenhagen presenting "Bayesian Autoencoders" (link)

25-07-23  Poster presentation at ICML of our paper "Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes" (link)

23-07-23  Delighted to be an invited speaker at the AABI workshop at ICML, presenting "Bayesian Autoencoders" (link)

30-05-23  Check out our new paper "One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models" (link)

29-05-23  I'm visiting NTNU University this week, acting as opponent for a Ph.D. thesis and presenting "Functional Priors for Bayesian Deep Learning"

29-05-23  I'm visiting CTU and the UTIA Institute in Prague this week, presenting "Bayesian Deep Learning"

25-04-23  The paper "Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes" has been accepted at ICML 2023! (pdf)

29-03-23  The paper "How Much is Enough? A Study on Diffusion Times in Score-based Generative Models" has been accepted in the Entropy Journal! (link)

01-03-23  Check out our new paper "Continuous-Time Functional Diffusion Processes" - joint work with M. Heinonen at Aalto (link)

09-02-23  Check out our new paper "Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes" - joint work with S. Mandt and B. Shahbaba at UCI (link)

18-01-23  I'm visiting Aalto University this week, acting as opponent for a Ph.D. thesis and presenting Functional priors for Bayesian Deep Learning

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