Maurizio Filippone
29 papers · 2013–2025 · 5 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (48) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Mega-Team Β· Squad
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Deep Specialist
(16)
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Topic Evolution
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Keyword Pioneer
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Dynamic Duo
(11)
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Century Club Β· Bronze
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Keyword Collector
(123)
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Trend Setter
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Unstoppable Β· Silver
Top co-authors
Research topics
Machine Learning
(27)
Optimization & Theory
(21)
Artificial Intelligence
(17)
Deep Learning
(14)
Bayesian & Probabilistic Β· Artificial Intelligence
(13)
Bayesian & Probabilistic Β· Machine Learning
(10)
Core Methods
(8)
Models
(8)
Mathematics & Optimization
(4)
Architectures
(4)
Core AI
(4)
Learning Types Β· Machine Learning
(4)
Learning Types Β· Deep Learning
(4)
Mathematics
(3)
Optimization
(3)
Keywords
gaussian process
(8)
variational inference
(8)
uncertainty quantification
(7)
bayesian inference
(5)
markov chain monte carlo
(4)
bayesian deep learning
(3)
latent space
(3)
variational autoencoder
(3)
sparse approximation
(3)
inducing variable
(2)
foundation model
(2)
bayesian neural network
(2)
sparse gaussian process
(2)
generative model
(2)
image classification
(2)
hamiltonian monte carlo
(2)
representation learning
(2)
stochastic variational inference
(2)
random feature
(2)
deep gaussian process
(2)
Papers
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
ICML 2025
Robust Classification by Coupling Data Mollification with Label Smoothing
AISTATS 2025
Unconditionally Calibrated Priors for Beta Mixture Density Networks
AISTATS 2025
Zero-shot Model-based Reinforcement Learning using Large Language Models
ICLR 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
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel
AISTATS 2023
Continuous-Time Functional Diffusion Processes
NeurIPS 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
ICML 2023
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
NeurIPS 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
An Identifiable Double VAE For Disentangled Representations
ICML 2021
Model Selection for Bayesian Autoencoders
NeurIPS 2021
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
AISTATS 2021
Sparse within Sparse Gaussian Processes using Neighbor Information
ICML 2021
LIBRE: Learning Interpretable Boolean Rule Ensembles
AISTATS 2020
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
NeurIPS 2020
Calibrating Deep Convolutional Gaussian Processes
AISTATS 2019
Good Initializations of Variational Bayes for Deep Models
ICML 2019
Pseudo-Extended Markov chain Monte Carlo
NeurIPS 2019
Constraining the Dynamics of Deep Probabilistic Models
ICML 2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
NeurIPS 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
NeurIPS 2015
ODE parameter inference using adaptive gradient matching with Gaussian processes
AISTATS 2013