Maurizio Filippone

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

Achievements

+10 more ↓

🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (16) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird

🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🌍 Conference Polyglot (5) 🤝 Dynamic Duo (11) 👥 Mega-Team (25) 🔬 Deep Specialist (12) 🏆 Keyword Champion 🔥 Unstoppable (11) 🗃️ Keyword Collector (100) 💎 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