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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
Check out our new book on ‘Bayesian Deep Learning’
Published:
Our new “Handbook of Bayesian Deep Learning” is now available online (link)
Talk at the SMILES summer schools ‘Bayesian Deep Learning’
Published:
Invited talk at the ‘SMILES summer school of machine learning’ in Nanjing University: “Bayesian Deep Learning” (link)
Talk at ISBA ‘Model Selection for Over-Parameterized Models’
Published:
Invited talk at the ‘Calibrated Bayes: Model Design and Adaptation Under Limited Resources’ session at ISBA in Nagoya: “Model Selection for Over-Parameterized Models” (link)
Paper ‘Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions’ accepted in DeCarbon
Published:
Our paper “Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions” has been accepted for publication in the journal DeCarbon (link)
Check out our new paper ‘DIPHINE: Diffusion-based Φ-ID Neural Estimator’ (link)
Published:
Check out our new paper “DIPHINE: Diffusion-based Φ-ID Neural Estimator” (link)
Paper ‘Position: agentic AI orchestration should be Bayes-consistent’ accepted at ICML 2026
Published:
Our position paper “Position: agentic AI orchestration should be Bayes-consistent” has been accepted for publication at ICML 2026 (link)
KAUST Rising Stars in AI Symposium 2026
Published:
The KAUST Rising Stars in AI Symposium 2026 is happening this week (link)
Talk at the AMCS-STAT school at KAUST: ‘Bayesian Deep Learning’
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Talk at the AMCS-STAT school at KAUST: “Bayesian Deep Learning”
Paper ‘Optimizing Data Augmentation through Bayesian Model Selection’ accepted at ICLR 2026
Published:
The paper “Optimizing Data Augmentation through Bayesian Model Selection” has been accepted at ICLR 2026! (link)
Paper ‘TENDE: Transfer Entropy Neural Diffusion Estimation’ accepted at AISTATS 2026
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The paper “TENDE: Transfer Entropy Neural Diffusion Estimation” has been accepted at AISTATS 2026! (link)
Participating in the workshop ‘Rethinking the Role of Bayesianism in the Age of Modern AI’ at MBZUAI
Published:
Participating in the workshop “Rethinking the Role of Bayesianism in the Age of Modern AI” at MBZUAI, Abu Dhabi, UAE (link)
Check out our new paper ‘TENDE: Transfer Entropy Neural Diffusion Estimation’ (link)
Published:
Check out our new paper “TENDE: Transfer Entropy Neural Diffusion Estimation” (link)
Talk at the Huawei Lavender Summit in Chantilly, France: ‘Bayesian Deep Learning’
Published:
Talk at the Huawei Lavender Summit in Chantilly, France: “Bayesian Deep Learning”
Check out our new paper ‘Universal Adaptive Environment Discovery’ (link)
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Check out our new paper “Universal Adaptive Environment Discovery” (link)
Check out our new paper ‘From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelih
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Check out our new paper “From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation” (link)
Poster at the GenU workshop in Copenhagen, Denmark ‘GANs Secretly Perform Approximate Bayesian Model
Published:
Poster at the GenU workshop in Copenhagen, Denmark “GANs Secretly Perform Approximate Bayesian Model Selection” (link)
Check out our new paper ‘GANs Secretly Perform Approximate Bayesian Model Selection’ (link)
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Check out our new paper “GANs Secretly Perform Approximate Bayesian Model Selection” (link)
Participating in the workshop ‘Bayesian Learning of Very High-Dimensional Physical Process Models’ a
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Participating in the workshop “Bayesian Learning of Very High-Dimensional Physical Process Models” at the Matrix Institute in Creswick, Australia (link)
Talk at the ‘One World Approximate Bayesian Inference (ABI) Seminar’ - live at BayesComp: ‘GANs Secr
Published:
Talk at the “One World Approximate Bayesian Inference (ABI) Seminar” - live at BayesComp: “GANs Secretly Perform Approximate Bayesian Model Selection” (link)
Check out our new paper ‘Scaling Laws for Uncertainty in Deep Learning’ (link)
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Check out our new paper “Scaling Laws for Uncertainty in Deep Learning” (link)
Check out our new paper ‘Variational Inference for Quantum HyperNetworks’ (link)
Published:
Check out our new paper “Variational Inference for Quantum HyperNetworks” (link)
Talk at KTH, Stockholm: ‘Priors for Bayesian Deep Learning’
Published:
Talk at KTH, Stockholm: “Priors for Bayesian Deep Learning”
Check out our new paper ‘Optimizing Data Augmentation through Bayesian Model Selection’ (link)
Published:
Check out our new paper “Optimizing Data Augmentation through Bayesian Model Selection” (link)
The paper ‘AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series F
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The paper “AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting” has been accepted at ICML 2025! (link)
The paper ‘Zero-shot Model-based Reinforcement Learning using Large Language Models’ has been accept
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The paper “Zero-shot Model-based Reinforcement Learning using Large Language Models” has been accepted at ICLR 2025! (link)
The paper ‘Unconditionally Calibrated Priors for Beta Mixture Density Networks’ has been accepted at
Published:
The paper “Unconditionally Calibrated Priors for Beta Mixture Density Networks” has been accepted at AISTATS 2025! (link)
The paper ‘Robust Classification by Coupling Data Mollification with Label Smoothing’ has been accep
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The paper “Robust Classification by Coupling Data Mollification with Label Smoothing” has been accepted at AISTATS 2025! (link)
Applied Mathematics School at KAUST happening this week (link)
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Applied Mathematics School at KAUST happening this week (link)
Workshop of Statistics at KAUST happening this week (link)
Published:
Workshop of Statistics at KAUST happening this week (link)
Participating in the Dagstuhl seminar Rethinking the Role of Bayesianism in the Age of Modern AI org
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Participating in the Dagstuhl seminar Rethinking the Role of Bayesianism in the Age of Modern AI organized by V. Fortuin, Z. Ghahramani, M. E. Khan, M. van der Wilk (link)
The paper ‘Improved Random Features for Dot Product Kernels’ has been accepted for publication in JM
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The paper “Improved Random Features for Dot Product Kernels” has been accepted for publication in JMLR! (link)
Invited talk at the session on Advances in Inference and Theory for Bayesian Neural Networks at JSM
Published:
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)
Invited talk at the ‘Recent Advances on High Dimensional Models’ session of the ICSDS conference in
Published:
Invited talk at the “Recent Advances on High Dimensional Models” session of the ICSDS conference in Nice: “Improving Optimization of Likelihood-based Generative Models with One Line of Code” (link)
Invited talk at the Approximate Inference in Theory and Practice workshop in Paris: ‘Improving Optim
Published:
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)
The position paper entitled ‘Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Published:
The position paper entitled “Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI” has been accepted at ICML 2024! (link)
The paper ‘Spatial Bayesian neural networks’ has been accepted in the Spatial Statistics journal! (l
Published:
The paper “Spatial Bayesian neural networks” has been accepted in the Spatial Statistics journal! (link)
I moved to KAUST as an Associate Professor of Statistics!
Published:
I moved to KAUST as an Associate Professor of Statistics!
The paper ‘One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative
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The paper “One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models” has been accepted at NeurIPS 2023! (link)
The paper ‘Continuous-Time Functional Diffusion Processes’ has been accepted at NeurIPS 2023! (link)
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The paper “Continuous-Time Functional Diffusion Processes” has been accepted at NeurIPS 2023! (link)
Delighted to be an invited speaker at the GenU workshop in Copenhagen presenting ‘Bayesian Autoencod
Published:
Delighted to be an invited speaker at the GenU workshop in Copenhagen presenting “Bayesian Autoencoders” (link)
Poster presentation at ICML of our paper ‘Fully Bayesian Autoencoders with Latent Sparse Gaussian Pr
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Poster presentation at ICML of our paper “Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes” (link)
Delighted to be an invited speaker at the AABI workshop at ICML, presenting ‘Bayesian Autoencoders’
Published:
Delighted to be an invited speaker at the AABI workshop at ICML, presenting “Bayesian Autoencoders” (link)
Check out our new paper ‘One-Line-of-Code Data Mollification Improves Optimization of Likelihood-bas
Published:
Check out our new paper “One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models” (link)
I’m visiting NTNU University this week, acting as opponent for a Ph.D. thesis and presenting ‘Functi
Published:
I’m visiting NTNU University this week, acting as opponent for a Ph.D. thesis and presenting “Functional Priors for Bayesian Deep Learning”
I’m visiting CTU and the UTIA Institute in Prague this week, presenting ‘Bayesian Deep Learning’
Published:
I’m visiting CTU and the UTIA Institute in Prague this week, presenting “Bayesian Deep Learning”
The paper ‘Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes’ has been accepted at I
Published:
The paper “Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes” has been accepted at ICML 2023! (pdf)
The paper ‘How Much is Enough? A Study on Diffusion Times in Score-based Generative Models’ has been
Published:
The paper “How Much is Enough? A Study on Diffusion Times in Score-based Generative Models” has been accepted in the Entropy Journal! (link)
Check out our new paper ‘Continuous-Time Functional Diffusion Processes’ - joint work with M. Heinon
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Check out our new paper “Continuous-Time Functional Diffusion Processes” - joint work with M. Heinonen at Aalto (link)
Check out our new paper ‘Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes’ - joint
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Check out our new paper “Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes” - joint work with S. Mandt and B. Shahbaba at UCI (link)
I’m visiting Aalto University this week, acting as opponent for a Ph.D. thesis and presenting Functi
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I’m visiting Aalto University this week, acting as opponent for a Ph.D. thesis and presenting Functional priors for Bayesian Deep Learning
Webinar with the discussion of the Bayesian Analysis paper ‘Deep Gaussian Processes for Calibration
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Webinar with the discussion of the Bayesian Analysis paper “Deep Gaussian Processes for Calibration of Computer Models”. (link)
Poster presentation at NeurIPS of our JMLR paper ‘All You Need is a Good Functional Prior for Bayesi
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Poster presentation at NeurIPS of our JMLR paper “All You Need is a Good Functional Prior for Bayesian Deep Learning” (link)
Talk at the workshop on Statistical Deep Learning Functional priors for Bayesian deep learning (link
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Talk at the workshop on Statistical Deep Learning Functional priors for Bayesian deep learning (link)
I will visit Data61 in Sydney and University of Wollongong over the next two weeks.
Published:
I will visit Data61 in Sydney and University of Wollongong over the next two weeks.
Participating in the Dagstuhl seminar Differential Equations and Continuous-Time Deep Learning organ
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Participating in the Dagstuhl seminar Differential Equations and Continuous-Time Deep Learning organized by M. Welling, D. Duvenaud, M. Heinonen and M. Tiemann (link)
The paper ‘Deep Gaussian Processes for Calibration of Computer Models’ has been selected to be a dis
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The paper “Deep Gaussian Processes for Calibration of Computer Models” has been selected to be a discussion paper! (pdf)
The paper ‘Revisiting the Effects of Stochasticity for Hamiltonian Samplers’ has been accepted at IC
Published:
The paper “Revisiting the Effects of Stochasticity for Hamiltonian Samplers” has been accepted at ICML 2022! (pdf)
The paper ‘All You Need is a Good Functional Prior for Bayesian Deep Learning’ will appear in JMLR!
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The paper “All You Need is a Good Functional Prior for Bayesian Deep Learning” will appear in JMLR! (pdf)
Participating in the Dagstuhl seminar Probabilistic Numerical Methods – From Theory to Implementatio
Published:
Participating in the Dagstuhl seminar Probabilistic Numerical Methods – From Theory to Implementation organized by P. Hennig, I. Ipsen, M. Mahsereci, T. Sullivan (link)
The paper ‘Model Selection for Bayesian Autoencoders’ has been accepted at NeurIPS 2021! (pdf)
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The paper “Model Selection for Bayesian Autoencoders” has been accepted at NeurIPS 2021! (pdf)
The paper ‘Deep Gaussian Processes for Calibration of Computer Models’ has been accepted in the Baye
Published:
The paper “Deep Gaussian Processes for Calibration of Computer Models” has been accepted in the Bayesian Analysis journal! (pdf)
Two papers accepted at ICML 2021 (link)
Published:
The paper ‘Deep Compositional Spatial Models’ has been accepted in the Journal of the American Stati
Published:
The paper “Deep Compositional Spatial Models” has been accepted in the Journal of the American Statistical Association! Joint work with A. Zammit-Mangion at Wollongong. (pdf) (link)
The paper ‘Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximati
Published:
The paper “Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations” has been accepted at AISTATS 2021! Joint work with M. Heinonen at Aalto (pdf)
Check out our new paper ‘All You Need is a Good Functional Prior for Bayesian Deep Learning’ (link)
Published:
Check out our new paper “All You Need is a Good Functional Prior for Bayesian Deep Learning” (link)
Check out our new paper ‘Sparse within Sparse Gaussian Processes using Neighbor Information’ (link)
Published:
Check out our new paper “Sparse within Sparse Gaussian Processes using Neighbor Information” (link)
Check out our new paper ‘An Identifiable Double VAE For Disentangled Representations’ (link)
Published:
Check out our new paper “An Identifiable Double VAE For Disentangled Representations” (link)
The paper ‘Walsh-Hadamard Variational Inference for Bayesian Deep Learning’ has been accepted at Neu
Published:
The paper “Walsh-Hadamard Variational Inference for Bayesian Deep Learning” has been accepted at NeurIPS 2020! (pdf) (code)
Check out our new paper ‘Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling’ (link)
Published:
Check out our new paper “Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling” (link)
Check out our new paper ‘A Variational View on Bootstrap Ensembles as Bayesian Inference’ (link)
Published:
Check out our new paper “A Variational View on Bootstrap Ensembles as Bayesian Inference” (link)
The paper ‘Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting’ has been
Published:
The paper “Model Monitoring and Dynamic Model Selection in Travel Time-series Forecasting” has been accepted at ECML/PKDD 2020! (pdf)
We are very excited to host the second workshop on ‘Functional Inference and Machine Intelligence’ a
Published:
We are very excited to host the second workshop on “Functional Inference and Machine Intelligence” at EURECOM - the event is co-organized with the Institute of Statistical Mathematics, Tokyo! (link)
The paper ‘Kernel computations from large-scale random features obtained by Optical Processing Units
Published:
The paper “Kernel computations from large-scale random features obtained by Optical Processing Units” has been accepted at ICASSP 2020! Joint work with LightOn. (pdf)
The paper ‘LIBRE: Learning Interpretable Boolean Rule Ensembles’ has been accepted at AISTATS 2020!
Published:
The paper “LIBRE: Learning Interpretable Boolean Rule Ensembles” has been accepted at AISTATS 2020! (pdf)
The paper ‘A comparative evaluation of novelty detection algorithms for discrete sequences’ has been
Published:
The paper “A comparative evaluation of novelty detection algorithms for discrete sequences” has been accepted for publication in the Artificial Intelligence Review journal! (link)
Talk at the Department of Statistics, University of Oxford, UK: ‘Walsh-Hadamard Variational Inferenc
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Talk at the Department of Statistics, University of Oxford, UK: “Walsh-Hadamard Variational Inference for Bayesian Deep Learning” (slides)
The paper ‘Efficient Approximate Inference with Walsh-Hadamard Variational Inference’ has been accep
Published:
The paper “Efficient Approximate Inference with Walsh-Hadamard Variational Inference” has been accepted at the Bayesian Deep Learning Workshop at NeurIPS 2019! (link) (pdf)
The paper ‘Pseudo-Extended Markov chain Monte Carlo’ has been accepted at NeurIPS 2019! (link) (pdf)
Published:
The paper “Pseudo-Extended Markov chain Monte Carlo” has been accepted at NeurIPS 2019! (link) (pdf)
Talk at the Deep Bayes Summer School in Moscow: ‘Deep Gaussian Processes’ (slides)
Published:
Talk at the Deep Bayes Summer School in Moscow: “Deep Gaussian Processes” (slides)
Read about my new research efforts on sustainable AI on The Conversation website: Light, a possible
Published:
Read about my new research efforts on sustainable AI on The Conversation website: Light, a possible solution for a sustainable AI
Tutorial at IJCNN 2019, Budapest, Hungary (link)
Published:
Tutorial at IJCNN 2019, Budapest, Hungary (link)
Talk at the Machine Learning Crash Course MLCC 2019 in Genova: ‘Introduction to Gaussian Processes’
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Talk at the Machine Learning Crash Course MLCC 2019 in Genova: “Introduction to Gaussian Processes” (slides)
Talk and poster at ICML 2019, Long Beach (CA), USA (slides) (poster)
Published:
Talk and poster at ICML 2019, Long Beach (CA), USA (slides) (poster)
The paper ‘Good Initializations of Variational Bayes for Deep Models’ has been accepted at ICML 2019
Published:
The paper “Good Initializations of Variational Bayes for Deep Models” has been accepted at ICML 2019! (link) (pdf)
Poster presentation at AISTATS 2019, Naha, Japan. (poster)
Published:
Poster presentation at AISTATS 2019, Naha, Japan. (poster)
Talk at the Northern Lights Deep Learning Workshop in Tromsø: ‘Bayesian Deep Learning’ (slide
Published:
Talk at the Northern Lights Deep Learning Workshop in Tromsø: “Bayesian Deep Learning” (slides)
The paper ‘Calibrating Deep Convolutional Gaussian Processes’ has been accepted at AISTATS 2019! (pd
Published:
The paper “Calibrating Deep Convolutional Gaussian Processes” has been accepted at AISTATS 2019! (pdf)
Check out our new paper ‘Variational Calibration of Computer Models’ (link)
Published:
Check out our new paper “Variational Calibration of Computer Models” (link)
Check out our new paper ‘Good Initializations of Variational Bayes for Deep Models’ (link)
Published:
Check out our new paper “Good Initializations of Variational Bayes for Deep Models” (link)
New PhD position available in my group (Ph.D. announcement)
Published:
New PhD position available in my group (Ph.D. announcement)
The paper ‘Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification’ has been ac
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The paper “Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification” has been accepted at NeurIPS 2018! (link) (pdf)
Talk at the Deep Bayes Summer School in Moscow: ‘Deep Gaussian Processes’ (slides)
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Talk at the Deep Bayes Summer School in Moscow: “Deep Gaussian Processes” (slides)
Talk and poster at ICML 2018, Stockholm, Sweden (slides) (poster)
Published:
Talk and poster at ICML 2018, Stockholm, Sweden (slides) (poster)
ANR-JCJC Grant Award: ‘ECO-ML: Rethinking Modern Machine Learning Tools for a New Generation of Low-
Published:
ANR-JCJC Grant Award: “ECO-ML: Rethinking Modern Machine Learning Tools for a New Generation of Low-Power Large-Scale Modeling Systems” (link)
I’ve been promoted to Associate Professor!
Published:
I’ve been promoted to Associate Professor!
The paper ‘Deep Gaussian Process Autoencoders for Novelty Detection’ has been accepted for publicati
Published:
The paper “Deep Gaussian Process Autoencoders for Novelty Detection” has been accepted for publication in the Machine Learning journal and it will be presented at the next ECML/PKDD conference! (link)
Check out our new paper ‘Dirichlet-based Gaussian Processes for Large-scale Calibrated Classificatio
Published:
Check out our new paper “Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification” (link)
Check out our new paper ‘Calibrating Deep Convolutional Gaussian Processes’ (link)
Published:
Check out our new paper “Calibrating Deep Convolutional Gaussian Processes” (link)
The paper ‘Constraining the Dynamics of Deep Probabilistic Models’ has been accepted at ICML 2018! (
Published:
The paper “Constraining the Dynamics of Deep Probabilistic Models” has been accepted at ICML 2018! (link) (pdf)
Talk at the Global Tehnical Forum at Amadeus: ‘Probabilistic Deep Learning’
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Talk at the Global Tehnical Forum at Amadeus: “Probabilistic Deep Learning”
New PhD position available in my group (Ph.D. announcement)
Published:
New PhD position available in my group (Ph.D. announcement)
Check out our new paper ‘Constraining the Dynamics of Deep Probabilistic Models’ (link)
Published:
Check out our new paper “Constraining the Dynamics of Deep Probabilistic Models” (link)
Lecture at the ‘Emerging Topics in Translational Bioinformatics’ workshop, Berlin: ‘Deep Gaussian P
Published:
Lecture at the “Emerging Topics in Translational Bioinformatics” workshop, Berlin: “Deep Gaussian Processes” (slides part 1) (slides part 2)
Talk at the ‘Surrogate models for UQ in complex systems’ workshop at the Isaac Newton Institute, Cam
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Talk at the “Surrogate models for UQ in complex systems” workshop at the Isaac Newton Institute, Cambridge: “Random Feature Expansions for Deep Gaussian Processes” (slides)
Talk at the University of Genova: ‘Random Feature Expansions for Deep Gaussian Processes’ (slides)
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Talk at the University of Genova: “Random Feature Expansions for Deep Gaussian Processes” (slides)
Talk at Imperial College, London: ‘Random Feature Expansions for Deep Gaussian Processes’ (slides)
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Talk at Imperial College, London: “Random Feature Expansions for Deep Gaussian Processes” (slides)
Talk at Prowler.io, Cambridge: ‘Random Feature Expansions for Deep Gaussian Processes’ (slides)
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Talk at Prowler.io, Cambridge: “Random Feature Expansions for Deep Gaussian Processes” (slides)
Talk at the University of Geneva, Switzerland: ‘Gaussian Processes and Some Applications in Neuroima
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Talk at the University of Geneva, Switzerland: “Gaussian Processes and Some Applications in Neuroimaging” (slides)
The paper A comparative evaluation of outlier detection algorithms: experiments and analyses has bee
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The paper A comparative evaluation of outlier detection algorithms: experiments and analyses has been accepted for publication in Pattern Recognition! (link)
Lecture at the Workshop on Stochastic Processes and Probabilistic Models in Machine Learning, Higher
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Lecture at the Workshop on Stochastic Processes and Probabilistic Models in Machine Learning, Higher School of Economics, Moscow: “Gaussian Processes” (slides)
The paper Probabilistic disease progression modeling to characterize diagnostic uncertainty: staging
Published:
The paper Probabilistic disease progression modeling to characterize diagnostic uncertainty: staging and prediction in Alzheimer’s disease has been accepted for publication in NeuroImage! (link)
Check out our new paper ‘Pseudo-extended Markov chain Monte Carlo’ joint work with C. Nemeth, F. Lin
Published:
Check out our new paper “Pseudo-extended Markov chain Monte Carlo” joint work with C. Nemeth, F. Lindsten and J. Hensman (link)
The paper ‘Entropic Trace Estimates for Log Determinants’ has been accepted at ECML 2017! (link)
Published:
The paper “Entropic Trace Estimates for Log Determinants” has been accepted at ECML 2017! (link)
The paper ‘Bayesian Inference of Log Determinants’ has been accepted at UAI 2017! (link)
Published:
The paper “Bayesian Inference of Log Determinants” has been accepted at UAI 2017! (link)
The paper ‘AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models’ has been a
Published:
The paper “AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models” has been accepted at UAI 2017! (link)
Talk at the AXA Data Innovation Lab - Paris: ‘Bayesian Inference: Challenges from Modern Application
Published:
Talk at the AXA Data Innovation Lab - Paris: “Bayesian Inference: Challenges from Modern Applications”
The paper ‘Random Feature Expansions for Deep Gaussian Processes’ has been accepted at ICML 2017! (l
Published:
The paper “Random Feature Expansions for Deep Gaussian Processes” has been accepted at ICML 2017! (link) (pdf) (code)
Talk at the OQUAIDO scientific meeting at the University of Nice: ‘Unbiased computations for tractab
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Talk at the OQUAIDO scientific meeting at the University of Nice: “Unbiased computations for tractable and scalable learning of Gaussian processes”
Post-Doc and PhD positions available in my group (Post-Doc announcement) (Ph.D. announcement)
Published:
Post-Doc and PhD positions available in my group (Post-Doc announcement) (Ph.D. announcement)
Check out our new paper ‘Entropic Trace Estimates for Log Determinants’ joint work with J. Fitzsimon
Published:
Check out our new paper “Entropic Trace Estimates for Log Determinants” joint work with J. Fitzsimons, D. Granziol, K. Cutajar, M. Osborne, and S. Roberts (link)
Talk at the École Centrale de Lille, France: ‘Unbiased computations for tractable and scalabl
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Talk at the École Centrale de Lille, France: “Unbiased computations for tractable and scalable learning of Gaussian processes”
Talk at Yandex, Moscow: ‘Practical and Scalable Inference for Deep Gaussian Processes’ (slides)
Published:
Talk at Yandex, Moscow: “Practical and Scalable Inference for Deep Gaussian Processes” (slides)
Check out our new paper ‘Bayesian Inference of Log Determinants’ joint work with J. Fitzsimons, K. C
Published:
Check out our new paper “Bayesian Inference of Log Determinants” joint work with J. Fitzsimons, K. Cutajar, M. Osborne, and S. Roberts (link)
Talk at Google Research, NYC: ‘Unbiased computations for tractable and scalable learning of Gaussian
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Talk at Google Research, NYC: “Unbiased computations for tractable and scalable learning of Gaussian processes”
Talk at the Mascot Num 2017 Meeting, Paris: ‘Practical and Scalable Inference for Deep Gaussian Proc
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Talk at the Mascot Num 2017 Meeting, Paris: “Practical and Scalable Inference for Deep Gaussian Processes” (slides)
The paper ‘Mini-Batch Spectral Clustering’ has been accepted at IJCNN 2017! (pdf) (code)
Published:
The paper “Mini-Batch Spectral Clustering” has been accepted at IJCNN 2017! (pdf) (code)
Check out our new paper ‘Disease Progression Modeling and Prediction through Random Effect Gaussian
Published:
Check out our new paper “Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation” joint work with M. Lorenzi, D. C. Alexander, S. Ourselin (link)
The paper ‘Adaptive Multiple Importance Sampling for Gaussian Processes’ has been accepted for publi
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The paper “Adaptive Multiple Importance Sampling for Gaussian Processes” has been accepted for publication in the Journal of Statistical Computation and Simulation!
The paper ‘Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels’ has been accepted
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The paper “Accelerating Deep Gaussian Processes Inference with Arc-Cosine Kernels” has been accepted at the Bayesian Deep Learning Workshop at NIPS 2016! (pdf) (link)
Fully funded PhD scholarship in Bayesian nonparametrics (link)
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Fully funded PhD scholarship in Bayesian nonparametrics (link)
Check out our new paper ‘Random Feature Expansions for Deep Gaussian Processes’ - joint work with K.
Published:
Check out our new paper “Random Feature Expansions for Deep Gaussian Processes” - joint work with K. Cutajar, P. Michiardi and E. V. Bonilla (link) (code)
Check out our new paper ‘AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Mode
Published:
Check out our new paper “AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models” - joint work with K. Krauth, K. Cutajar, and E. V. Bonilla (link)
The paper ‘Decoding post-stroke motor function from structural brain imaging’ has been accepted for
Published:
The paper “Decoding post-stroke motor function from structural brain imaging” has been accepted for publication in NeuroImage: Clinical! (link)
Check out our new paper ‘Mini-Batch Spectral Clustering’ (link)
Published:
Check out our new paper “Mini-Batch Spectral Clustering” (link)
The paper ‘Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Perso
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The paper “Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions” has been accepted at ACM Multimedia 2016! (link)
Talk at TU Darmstadt, Germany: ‘Unbiased computations for tractable and scalable learning of Gaussia
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Talk at TU Darmstadt, Germany: “Unbiased computations for tractable and scalable learning of Gaussian processes”
Talk at the Donders Institute, The Netherlands: ‘Unbiased computations for tractable and scalable le
Published:
Talk at the Donders Institute, The Netherlands: “Unbiased computations for tractable and scalable learning of Gaussian processes”
The paper ‘Preconditioning Kernel Matrices’ has been accepted at ICML 2016! (pdf) (link) (code)
Published:
The paper “Preconditioning Kernel Matrices” has been accepted at ICML 2016! (pdf) (link) (code)
The paper ‘Fast Inference in Nonlinear Dynamical Systems using Gradient Matching’ has been accepted
Published:
The paper “Fast Inference in Nonlinear Dynamical Systems using Gradient Matching” has been accepted at ICML 2016! (pdf) (link)
I’ve been awarded an AXA Chair position for the duration of 7 years from the AXA Research Fund
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I’ve been awarded an AXA Chair position for the duration of 7 years from the AXA Research Fund
Talk at KTH, Sweden: ‘Unbiased computations for tractable and scalable learning of Gaussian processe
Published:
Talk at KTH, Sweden: “Unbiased computations for tractable and scalable learning of Gaussian processes”
Check out our new paper ‘Preconditioning Kernel Matrices’ - joint work with K. Cutajar, M. A. Osborn
Published:
Check out our new paper “Preconditioning Kernel Matrices” - joint work with K. Cutajar, M. A. Osborne and J. P. Cunningham (link) (code)
Poster at NIPS 2015 (poster)
Published:
Our paper on Monte Carlo estimation of password strength featured in the MIT Technology Review websi
Published:
Our paper on Monte Carlo estimation of password strength featured in the MIT Technology Review website. (link)
‘Special Mention’ award for the poster at the Autumn meeting on Latent Gaussian Models 2015 in Trond
Published:
“Special Mention” award for the poster at the Autumn meeting on Latent Gaussian Models 2015 in Trondheim, Norway (poster)
The paper ‘MCMC for Variationally Sparse Gaussian Processes’ has been accepted at NIPS! (link)
Published:
The paper “MCMC for Variationally Sparse Gaussian Processes” has been accepted at NIPS! (link)
The paper ‘Adaptive Multiple Importance Sampling for Gaussian Processes’ is available online (link)
Published:
The paper “Adaptive Multiple Importance Sampling for Gaussian Processes” is available online (link)
The paper ‘Monte Carlo Strength Evaluation: Fast and Reliable Password Checking’ has been accepted a
Published:
The paper “Monte Carlo Strength Evaluation: Fast and Reliable Password Checking” has been accepted at the 22nd ACM Conference on Computer and Communications Security (CCS 2015)! (link)
Presentation at the Workshop on Bayesian Inference for Big Data BIBiD2015 in Oxford: ‘Scaling Bayesi
Published:
Presentation at the Workshop on Bayesian Inference for Big Data BIBiD2015 in Oxford: “Scaling Bayesian Inference for Gaussian Processes using the Stochastic Gradient Langevin Dynamics algorithm” (link)
Check out our new paper ‘MCMC for Variationally Sparse Gaussian Processes’ - joint work with James H
Published:
Check out our new paper “MCMC for Variationally Sparse Gaussian Processes” - joint work with James Hensman, Alexander G. de G. Matthews, and Zoubin Ghahramani (link)
Invited talk in the Department of Statistics at the University of Oxford: ‘Unbiased computations for
Published:
Invited talk in the Department of Statistics at the University of Oxford: “Unbiased computations for MCMC-based inference of Gaussian process covariance parameters” (link)
The paper ‘Enabling scalable stochastic gradient-based inference for Gaussian processes by employing
Published:
The paper “Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)” has been accepted at ICML! (link)
Invited talk at the University of Sheffield: ‘Unbiased computations for MCMC-based inference of Gaus
Published:
Invited talk at the University of Sheffield: “Unbiased computations for MCMC-based inference of Gaussian process covariance parameters”
I’ve been offered a job at EURECOM, and I’ll be leaving Glasgow in August.
Published:
I’ve been offered a job at EURECOM, and I’ll be leaving Glasgow in August.
The paper ‘Enabling scalable stochastic gradient-based inference for Gaussian processes by employing
Published:
The paper “Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)” is available online (link)
The paper ‘Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models’ ha
Published:
The paper “Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models” has been accepted for publication in the Annals of Applied Statistics (link)
Public engagement event at the Grosvenor Cinema (flyer)
Published:
Public engagement event at the Grosvenor Cinema (flyer)
Public engagement event at the Glasgow Film Theatre (article in the Herald) (link)
Published:
Public engagement event at the Glasgow Film Theatre (article in the Herald) (link)
Invited talk at Bristol University (SPHERE): ‘Pseudo-Marginal Bayesian Inference for Gaussian Proces
Published:
Invited talk at Bristol University (SPHERE): “Pseudo-Marginal Bayesian Inference for Gaussian Processes” (link)
Post-Doc position available for three years (link to job description)
Published:
Post-Doc position available for three years (link to job description)
Fully funded PhD position available in Bayesian inference for Gaussian Processes (link)
Published:
Fully funded PhD position available in Bayesian inference for Gaussian Processes (link)
Invited talk at the Informatics Forum at the University of Edinburgh: ‘Pseudo-Marginal Bayesian Infe
Published:
Invited talk at the Informatics Forum at the University of Edinburgh: “Pseudo-Marginal Bayesian Inference for Gaussian Processes” (link)
Invited talk in the Department of Statistics at Columbia University: ‘Pseudo-Marginal Bayesian Infer
Published:
Invited talk in the Department of Statistics at Columbia University: “Pseudo-Marginal Bayesian Inference for Gaussian Processes” (link)
The paper ‘On User Availability Prediction And Network Applications’ has been accepted for publicati
Published:
The paper “On User Availability Prediction And Network Applications” has been accepted for publication in the IEEE/ACM Transactions on Networking (link)
The paper ‘Pseudo-Marginal Bayesian Inference for Gaussian Processes’ has been accepted for publicat
Published:
The paper “Pseudo-Marginal Bayesian Inference for Gaussian Processes” has been accepted for publication in the IEEE Transactions on Pattern Analysis and Machine Intelligence (link)
Two papers accepted at ICPR 2014 (link)
Published:
EPSRC grant success (~£350K) with Prof Husmeier and Dr Rogers: ‘Computational inference of biopathwa
Published:
EPSRC grant success (~£350K) with Prof Husmeier and Dr Rogers: “Computational inference of biopathway dynamics and structures” (link)
The paper ‘Predicting Continuous Conflict Perception with Bayesian Gaussian Processes’ has been acce
Published:
The paper “Predicting Continuous Conflict Perception with Bayesian Gaussian Processes” has been accepted for publication in the IEEE Transactions on Affective Computing (link)
Invited talk at the Department of Economics and Statistics at the University of Turin: ‘Exact-Approx
Published:
Invited talk at the Department of Economics and Statistics at the University of Turin: “Exact-Approximate Bayesian Inference for Gaussian Processes”
Invited talk at UTIA, Prague: ‘Exact-Approximate Bayesian Inference for Gaussian Process Classifiers
Published:
Invited talk at UTIA, Prague: “Exact-Approximate Bayesian Inference for Gaussian Process Classifiers”
Keynote speech at the Conference on Electronics, Telecommunications and Computers in Lisbon (link)
Published:
Keynote speech at the Conference on Electronics, Telecommunications and Computers in Lisbon (link)
The paper ‘Bayesian Inference for Gaussian Process Classifiers with Annealing and Exact-Approximate
Published:
The paper “Bayesian Inference for Gaussian Process Classifiers with Annealing and Exact-Approximate MCMC” is available online (link)
The paper ‘Exact-Approximate Bayesian Inference for Gaussian Processes’ is available online (link)
Published:
The paper “Exact-Approximate Bayesian Inference for Gaussian Processes” is available online (link)
Presentation at ECML/PKDD 2013: ‘A Comparative Evaluation of Stochastic-based Inference Methods for
Published:
Presentation at ECML/PKDD 2013: “A Comparative Evaluation of Stochastic-based Inference Methods for Gaussian Process Models” (slides) (poster)
The paper ‘Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models’ is
Published:
The paper “Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models” is available online (link)
The paper ‘Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition
Published:
The paper “Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach” has been accepted for publication in PLoS ONE (link)
The paper ‘A Comparative Evaluation of Stochastic-based Inference Methods for Gaussian Process Model
Published:
The paper “A Comparative Evaluation of Stochastic-based Inference Methods for Gaussian Process Models” has been accepted for publication in the Machine Learning journal, and will be presented at ECML/PKDD 2013 (link)
The paper ‘ODE parameter inference using adaptive gradient matching with Gaussian processes’ has bee
Published:
The paper “ODE parameter inference using adaptive gradient matching with Gaussian processes” has been accepted to AISTATS 2013 for an oral presentation (link)
The paper ‘Aggregation Algorithm Towards Large-Scale Boolean Network Analysis’ has been accepted for
Published:
The paper “Aggregation Algorithm Towards Large-Scale Boolean Network Analysis” has been accepted for publication in the IEEE Transactions on Automatic Control (link)
I’ve been invited to join the Editorial Board of the IEEE Transactions on Neural Networks and Learni
Published:
I’ve been invited to join the Editorial Board of the IEEE Transactions on Neural Networks and Learning Systems from January 2013 (link)
I’ve been invited to join the Editorial Board of Pattern Recognition (link)
Published:
I’ve been invited to join the Editorial Board of Pattern Recognition (link)
A summary of the work I’ve done with S. Kim, F. Valente, and A. Vinciarelli featured in the New Scie
Published:
A summary of the work I’ve done with S. Kim, F. Valente, and A. Vinciarelli featured in the New Scientist website. (link)
The papers ‘Predicting the Conflict Level in Television Political Debates: an Approach Based on Crow
Published:
The papers “Predicting the Conflict Level in Television Political Debates: an Approach Based on Crowdsourcing, Nonverbal Communication and Gaussian Processes” and “From Speech to Personality: Mapping Voice Quality and Intonation into Personality Differences” have been accepted for publication in the Proceedings of ACM Multimedia 2012 (link)
The paper ‘Probabilistic Prediction of Neurological Disorders with a Statistical Assessment of Neuro
Published:
The paper “Probabilistic Prediction of Neurological Disorders with a Statistical Assessment of Neuroimaging Data Modalities” has been accepted for publication in the Annals of Applied Statistics (link)
Talk at the University of Turin: ‘On the Fully Bayesian Treatment of Latent Gaussian Models using St
Published:
Talk at the University of Turin: “On the Fully Bayesian Treatment of Latent Gaussian Models using Stochastic Simulations”
I will be presenting the work ‘On the Fully Bayesian Treatment of Latent Gaussian Models using Stoch
Published:
I will be presenting the work “On the Fully Bayesian Treatment of Latent Gaussian Models using Stochastic Simulations” at The Second Workshop on Bayesian Inference for Latent Gaussian Models with Applications in Trondheim (link) (slides)
The paper ‘On the Fully Bayesian Treatment of Latent Gaussian Models using Stochastic Simulations’ i
Published:
The paper “On the Fully Bayesian Treatment of Latent Gaussian Models using Stochastic Simulations” is available online (pdf)
Talk at the University of Glasgow: ‘Inference in hierarchical models using stochastic approximations
Published:
Talk at the University of Glasgow: “Inference in hierarchical models using stochastic approximations”
Prof Husmeier, Dr Rogers, and myself have been awarded the Bridging the Gap-EPSRC research grant: ‘M
Published:
Prof Husmeier, Dr Rogers, and myself have been awarded the Bridging the Gap-EPSRC research grant: “Method and software integration for systems biology” - £30K
I moved to the School of Computing Science of University of Glasgow
Published:
I moved to the School of Computing Science of University of Glasgow
Talk at the Italian Statistical Society Conference (Bologna, Italy) - Special session on Recent adva
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Talk at the Italian Statistical Society Conference (Bologna, Italy) - Special session on Recent advances in Bayesian statistics: MCMC and beyond: “Bayesian inference in latent variable models and applications” (slides)
The paper ‘Approximate Inference of the Bandwidth in Multivariate Kernel Density Estimation’ has bee
Published:
The paper “Approximate Inference of the Bandwidth in Multivariate Kernel Density Estimation” has been accepted for publication in Computational Statistics and Data Analysis (link)
I have been offered a Lectureship in the School of Computing Science at University of Glasgow, with
Published:
I have been offered a Lectureship in the School of Computing Science at University of Glasgow, with starting date in September.
Discussion of the paper ‘Sampling Schemes for Generalized Linear Dirichlet Process Random Effects Mo
Published:
Discussion of the paper “Sampling Schemes for Generalized Linear Dirichlet Process Random Effects Models” by M. Kyung, J. Gill, and G. Casella (pdf)
Talk at UCL (CSML seminars series): ‘Calibration of Oil Reservoir Simulation Codes’ (slides)
Published:
Talk at UCL (CSML seminars series): “Calibration of Oil Reservoir Simulation Codes” (slides)
Talk at UCL: ‘Classification of fMRI data using latent Gaussian models’ (slides)
Published:
Talk at UCL: “Classification of fMRI data using latent Gaussian models” (slides)
I moved to the Department of Statistical Science of University College London
Published:
I moved to the Department of Statistical Science of University College London
I presented the poster ‘Posterior Inference in Latent Gaussian Models Using Manifold MCMC Methods’ (
Published:
I presented the poster “Posterior Inference in Latent Gaussian Models Using Manifold MCMC Methods” (pdf) at the NIPS workshop Monte Carlo Methods for Bayesian Inference in Modern Day Applications
Royal statistical Society meeting - discussion of the paper ‘Riemann manifold Langevin and Hamiltoni
Published:
Royal statistical Society meeting - discussion of the paper “Riemann manifold Langevin and Hamiltonian Monte Carlo methods” by M. Girolami and B. Calderhead (Slides - Contribution 1 - Contribution 2)
The paper ‘A Perturbative Approach to Novelty Detection in Autoregressive Models’ has been accepted
Published:
The paper “A Perturbative Approach to Novelty Detection in Autoregressive Models” has been accepted for publication in the IEEE Transactions on Signal Processing (link)
I’ll be moving to the Department of Statistical Science of University College London, following Prof
Published:
I’ll be moving to the Department of Statistical Science of University College London, following Prof. Mark Girolami that will join UCL in November.
Best Paper Award - The paper ‘A survey of kernel and spectral methods for clustering’ has been chose
Published:
Best Paper Award - The paper “A survey of kernel and spectral methods for clustering” has been chosen to be the best paper published in 2008 in the journal Pattern Recognition. The award will be presented at ICPR 2010 in Istanbul (PR homepage) (link)
The paper ‘Applying the Possibilistic C-Means Algorithm in Kernel-Induced Spaces’ has been accepted
Published:
The paper “Applying the Possibilistic C-Means Algorithm in Kernel-Induced Spaces” has been accepted for publication in the IEEE Transactions on Fuzzy Systems (link)
I’ll be moving to Glasgow in January! I’ll be working with Prof. Mark Girolami with the inference gr
Published:
I’ll be moving to Glasgow in January! I’ll be working with Prof. Mark Girolami with the inference group.
Talk at University of Edinburgh: ‘Information Theoretic Novelty Detection’ (slides)
Published:
Talk at University of Edinburgh: “Information Theoretic Novelty Detection” (slides)
Tutorial (for the Speech and Hearing group): ‘The probabilistic approach in data modeling’ (slides a
Published:
Tutorial (for the Speech and Hearing group): “The probabilistic approach in data modeling” (slides and audio) (login required)
Talk at Columbia University: ‘Information Theoretic Novelty Detection’ (slides)
Published:
Talk at Columbia University: “Information Theoretic Novelty Detection” (slides)
I’ll be visiting Columbia University until 15-07-09
Published:
I’ll be visiting Columbia University until 15-07-09
The paper ‘Novelty detection in autoregressive models using information theoretic measures’ is avail
Published:
The paper “Novelty detection in autoregressive models using information theoretic measures” is available online (link)
The paper ‘Information Theoretic Novelty Detection’ has been accepted for publication in Pattern Rec
Published:
The paper “Information Theoretic Novelty Detection” has been accepted for publication in Pattern Recognition (link)
Talk at University of Sheffield: ‘Information Theoretic Novelty Detection’ (slides)
Published:
Talk at University of Sheffield: “Information Theoretic Novelty Detection” (slides)
portfolio
publications
talks
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.