← Authors

Maurizio Filippone

29 papers · 2013–2025 · 5 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (16) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🌍 Conference Polyglot (5) 🀝 Dynamic Duo (11) πŸ‘₯ Mega-Team (25) πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion πŸ”₯ Unstoppable (11) πŸ—ƒοΈ Keyword Collector (100) πŸ’Ž Century Club (29)

Conferences

ICML (12) AISTATS (7) NIPS (7) JMLR (2) ICLR (1)

Top co-authors

Pietro Michiardi (11) Simone Rossi (6) Dimitrios Milios (5) Ba-Hien Tran (5) Markus Heinonen (3) Giulio Franzese (3) Edwin V. Bonilla (3) John Cunningham (2) Gia-Lac Tran (2) Graziano Mita (2)

Keywords

variational inference (8) gaussian process (8) bayesian inference (5) uncertainty quantification (5) markov chain monte carlo (4) variational autoencoder (3) sparse approximation (3) polynomial kernel (2) sparse gaussian process (2) gradient matching (2) latent space (2) representation learning (2) deep gaussian process (2) random feature (2) bayesian deep learning (2) stochastic variational inference (2) hamiltonian monte carlo (2) generative model (2) inducing variable (2) hyperparameter learning (1)

Papers

Unconditionally Calibrated Priors for Beta Mixture Density Networks AISTATS 2025
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting ICML 2025
Zero-shot Model-based Reinforcement Learning using Large Language Models ICLR 2025
Robust Classification by Coupling Data Mollification with Label Smoothing AISTATS 2025
Improved Random Features for Dot Product Kernels JMLR 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Continuous-Time Functional Diffusion Processes NIPS 2023
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel AISTATS 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes ICML 2023
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models NIPS 2023
All You Need is a Good Functional Prior for Bayesian Deep Learning JMLR 2022
Revisiting the Effects of Stochasticity for Hamiltonian Samplers ICML 2022
Sparse within Sparse Gaussian Processes using Neighbor Information ICML 2021
An Identifiable Double VAE For Disentangled Representations ICML 2021
Model Selection for Bayesian Autoencoders NIPS 2021
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations AISTATS 2021
LIBRE: Learning Interpretable Boolean Rule Ensembles AISTATS 2020
Walsh-Hadamard Variational Inference for Bayesian Deep Learning NIPS 2020
Pseudo-Extended Markov chain Monte Carlo NIPS 2019
Good Initializations of Variational Bayes for Deep Models ICML 2019
Calibrating Deep Convolutional Gaussian Processes AISTATS 2019
Constraining the Dynamics of Deep Probabilistic Models ICML 2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification NIPS 2018
Random Feature Expansions for Deep Gaussian Processes ICML 2017
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching ICML 2016
Preconditioning Kernel Matrices ICML 2016
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE) ICML 2015
MCMC for Variationally Sparse Gaussian Processes NIPS 2015
ODE parameter inference using adaptive gradient matching with Gaussian processes AISTATS 2013