Maurizio Filippone

Motonobu Kanagawa

J. Mach. Learn. Res., 2024

Zero-shot Model-based Reinforcement Learning using Large Language Models.

[BibT_eX]

[DOI]

Abdelhakim Benechehab

Youssef Attia El Hili

CoRR, 2024

Robust Classification by Coupling Data Mollification with Label Smoothing.

[BibT_eX]

[DOI]

CoRR, 2024

A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning.

[BibT_eX]

[DOI]

Abdelhakim Benechehab

CoRR, 2024

Variational DAG Estimation via State Augmentation With Stochastic Permutations.

[BibT_eX]

[DOI]

CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.

[BibT_eX]

[DOI]

CoRR, 2024

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models.

[BibT_eX]

[DOI]

Entropy, April, 2023

Spatial Bayesian Neural Networks.

[BibT_eX]

[DOI]

Andrew Zammit-Mangion

CoRR, 2023

Multi-timestep models for Model-based Reinforcement Learning.

[BibT_eX]

[DOI]

Abdelhakim Benechehab

CoRR, 2023

When is Importance Weighting Correction Needed for Covariate Shift Adaptation?

[BibT_eX]

[DOI]

CoRR, 2023

Continuous-Time Functional Diffusion Processes.

[BibT_eX]

[DOI]

CoRR, 2023

One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Continuous-Time Functional Diffusion Processes.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Imposing Functional Priors on Bayesian Neural Networks.

[BibT_eX]

[DOI]

Bogdan L. Kozyrskiy

Dimitrios Milios

Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 2023

Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel.

[BibT_eX]

[DOI]

Ruben Ohana

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

All You Need is a Good Functional Prior for Bayesian Deep Learning.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Complex-to-Real Random Features for Polynomial Kernels.

[BibT_eX]

[DOI]

Ruben Ohana

CoRR, 2022

Local Random Feature Approximations of the Gaussian Kernel.

[BibT_eX]

[DOI]

Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES-2022, 2022

Variational Bootstrap for Classification.

[BibT_eX]

[DOI]

Bogdan L. Kozyrskiy

Dimitrios Milios

Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES-2022, 2022

Locally Smoothed Gaussian Process Regression.

[BibT_eX]

[DOI]

Davit Gogolashvili

Bogdan L. Kozyrskiy

Matthieu Da Silva-Filarder

Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES-2022, 2022

Revisiting the Effects of Stochasticity for Hamiltonian Samplers.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization.

[BibT_eX]

[DOI]

Entropy, 2021

A Unified View of Stochastic Hamiltonian Sampling.

[BibT_eX]

[DOI]

CoRR, 2021

Model Selection for Bayesian Autoencoders.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Multimodal Variational Autoencoders for Sensor Fusion and Cross Generation.

[BibT_eX]

[DOI]

Andrea Ancora

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

Sparse within Sparse Gaussian Processes using Neighbor Information.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

An Identifiable Double VAE For Disentangled Representations.

[BibT_eX]

[DOI]

Graziano Mita

Proceedings of the 38th International Conference on Machine Learning, 2021

Towards a generic deep learning pipeline for traffic measurements.

[BibT_eX]

[DOI]

Proceedings of the CoNEXT-SW '21: Proceedings of the CoNEXT Student Workshop, 2021

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning Optimal Conditional Priors For Disentangled Representations.

[BibT_eX]

[DOI]

Graziano Mita

CoRR, 2020

Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling.

[BibT_eX]

[DOI]

CoRR, 2020

A Variational View on Bootstrap Ensembles as Bayesian Inference.

[BibT_eX]

[DOI]

Dimitrios Milios

CoRR, 2020

Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations.

[BibT_eX]

[DOI]

CoRR, 2020

A comparative evaluation of novelty detection algorithms for discrete sequences.

[BibT_eX]

[DOI]

Artif. Intell. Rev., 2020

Model Monitoring and Dynamic Model Selection in Travel Time-Series Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track, 2020

Walsh-Hadamard Variational Inference for Bayesian Deep Learning.

[BibT_eX]

[DOI]

Simone Rossi

Sébastien Marmin

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

LIBRE: Learning Interpretable Boolean Rule Ensembles.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease.

[BibT_eX]

[DOI]

NeuroImage, 2019

Efficient Approximate Inference with Walsh-Hadamard Variational Inference.

[BibT_eX]

[DOI]

Simone Rossi

Sébastien Marmin

CoRR, 2019

Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD.

[BibT_eX]

[DOI]

CoRR, 2019

Exact gaussian process regression with distributed computations.

[BibT_eX]

[DOI]

Duc-Trung Nguyen

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019

Pseudo-Extended Markov chain Monte Carlo.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Good Initializations of Variational Bayes for Deep Models.

[BibT_eX]

[DOI]

Simone Rossi

Proceedings of the 36th International Conference on Machine Learning, 2019

Calibrating Deep Convolutional Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

A comparative evaluation of outlier detection algorithms: Experiments and analyses.

[BibT_eX]

[DOI]

Pattern Recognit., 2018

Deep Gaussian Process autoencoders for novelty detection.

[BibT_eX]

[DOI]

Mach. Learn., 2018

Statistical inference in mechanistic models: time warping for improved gradient matching.

[BibT_eX]

[DOI]

Comput. Stat., 2018

Variational Calibration of Computer Models.

[BibT_eX]

[DOI]

Sébastien Marmin

CoRR, 2018

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Constraining the Dynamics of Deep Probabilistic Models.

[BibT_eX]

[DOI]

Marco Lorenzi

Proceedings of the 35th International Conference on Machine Learning, 2018

Team Deep Neural Networks for Interference Channels.

[BibT_eX]

[DOI]

Paul de Kerret

David Gesbert

Proceedings of the 2018 IEEE International Conference on Communications Workshops, 2018

2017

Decentralized Deep Scheduling for Interference Channels.

[BibT_eX]

[DOI]

Paul de Kerret

David Gesbert

CoRR, 2017

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Bayesian Inference of Log Determinants.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Entropic Trace Estimates for Log Determinants.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Mini-batch spectral clustering.

[BibT_eX]

[DOI]

Yufei Han

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Random Feature Expansions for Deep Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Mini-Batch Spectral Clustering.

[BibT_eX]

[DOI]

Yufei Han

Yun Shen

CoRR, 2016

Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions.

[BibT_eX]

[DOI]

Xiaoyu Xiong

Alexander G. de G. Matthews

Proceedings of the 2016 ACM Conference on Multimedia Conference, 2016

Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching.

[BibT_eX]

[DOI]

Proceedings of the 33nd International Conference on Machine Learning, 2016

Preconditioning Kernel Matrices.

[BibT_eX]

[DOI]

Proceedings of the 33nd International Conference on Machine Learning, 2016

Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation.

[BibT_eX]

[DOI]

Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2016

Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching.

[BibT_eX]

[DOI]

Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2016

2015

On User Availability Prediction and Network Applications.

[BibT_eX]

[DOI]

IEEE/ACM Trans. Netw., 2015

MCMC for Variationally Sparse Gaussian Processes.

[BibT_eX]

[DOI]

James Hensman

Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE).

[BibT_eX]

[DOI]

Raphael Engler

Proceedings of the 32nd International Conference on Machine Learning, 2015

Monte Carlo Strength Evaluation: Fast and Reliable Password Checking.

[BibT_eX]

[DOI]

Matteo Dell'Amico

Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 2015

2014

Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes.

[BibT_eX]

[DOI]

Samuel Kim

Fabio Valente

IEEE Trans. Affect. Comput., 2014

Pseudo-Marginal Bayesian Inference for Gaussian Processes.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2014

Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

2013

Editorial A Successful Change From TNN to TNNLS and a Very Successful Year.

[BibT_eX]

[DOI]

Eduardo Bayro-Corrochano

IEEE Trans. Neural Networks Learn. Syst., 2013

Aggregation Algorithm Towards Large-Scale Boolean Network Analysis.

[BibT_eX]

[DOI]

Yin Zhao

Jongrae Kim

IEEE Trans. Autom. Control., 2013

A comparative evaluation of stochastic-based inference methods for Gaussian process models.

[BibT_eX]

[DOI]

Mingjun Zhong

Mach. Learn., 2013

Exact-Approximate Bayesian Inference for Gaussian Processes.

[BibT_eX]

[DOI]

CoRR, 2013

ODE parameter inference using adaptive gradient matching with Gaussian processes.

[BibT_eX]

[DOI]

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Towards Causal Modeling of Human Behavior.

[BibT_eX]

[DOI]

Proceedings of the Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets, 2012

From speech to personality: mapping voice quality and intonation into personality differences.

[BibT_eX]

[DOI]

Gelareh Mohammadi

Antonio Origlia

Proceedings of the 20th ACM Multimedia Conference, MM '12, Nara, Japan, October 29, 2012

Predicting the conflict level in television political debates: an approach based on crowdsourcing, nonverbal communication and gaussian processes.

[BibT_eX]

[DOI]

Samuel Kim

Fabio Valente

Proceedings of the 20th ACM Multimedia Conference, MM '12, Nara, Japan, October 29, 2012

2011

A Perturbative Approach to Novelty Detection in Autoregressive Models.

[BibT_eX]

[DOI]

Guido Sanguinetti

IEEE Trans. Signal Process., 2011

Simulated annealing for supervised gene selection.

[BibT_eX]

[DOI]

Soft Comput., 2011

Discussion of the paper: "Sampling schemes for generalized linear Dirichlet process random effects models" by M. Kyung, J. Gill, and G. Casella.

[BibT_eX]

[DOI]

Antonietta Mira

Stat. Methods Appl., 2011

Approximate inference of the bandwidth in multivariate kernel density estimation.

[BibT_eX]

[DOI]

Guido Sanguinetti

Comput. Stat. Data Anal., 2011

2010

Applying the Possibilistic c-Means Algorithm in Kernel-Induced Spaces.

[BibT_eX]

[DOI]

IEEE Trans. Fuzzy Syst., 2010

Information theoretic novelty detection.

[BibT_eX]

[DOI]

Guido Sanguinetti

Pattern Recognit., 2010

2009

A comparative evaluation of nonlinear dynamics methods for time series prediction.

[BibT_eX]

[DOI]

Francesco Camastra

Neural Comput. Appl., 2009

Soft ranking in clustering.

[BibT_eX]

[DOI]

Neurocomputing, 2009

Clustering in the membership embedding space.

[BibT_eX]

[DOI]

Int. J. Knowl. Eng. Soft Data Paradigms, 2009

Dealing with non-metric dissimilarities in fuzzy central clustering algorithms.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2009

The discriminating power of random features.

[BibT_eX]

[DOI]

Proceedings of the Neural Nets WIRN09, 2009

2008

A survey of kernel and spectral methods for clustering.

[BibT_eX]

[DOI]

Pattern Recognit., 2008

An Experimental Comparison of Kernel Clustering Methods.

[BibT_eX]

[DOI]

Proceedings of the New Directions in Neural Networks, 2008

Detecting Suspicious Behavior in Surveillance Images.

[BibT_eX]

[DOI]

Proceedings of the Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Stability and Performances in Biclustering Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2008

2007

Possibilistic Clustering in Feature Space.

[BibT_eX]

[DOI]

Proceedings of the Applications of Fuzzy Sets Theory, 2007

Membership Embedding Space Approach and Spectral Clustering.

[BibT_eX]

[DOI]

Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems, 2007

SVM-Based Time Series Prediction with Nonlinear Dynamics Methods.

[BibT_eX]

[DOI]

Francesco Camastra

Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems, 2007

Local Learning of Tide Level Time Series using a Fuzzy Approach.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2007

2006

Supervised Classification and Gene Selection Using Simulated Annealing.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2006

Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis.

[BibT_eX]

[DOI]

Proceedings of the Computational Methods in Systems Biology, International Conference, 2006

2005

Soft Rank Clustering.

[BibT_eX]

[DOI]

Proceedings of the Neural Nets, 16th Italian Workshop on Neural Nets, 2005

Unsupervised Gene Selection and Clustering Using Simulated Annealing.

[BibT_eX]

[DOI]

Proceedings of the Fuzzy Logic and Applications, 6th International Workshop, 2005

2004

ERAF: A R Package for Regression and Forecasting.

[BibT_eX]

[DOI]