An analysis prior based decomposition method for audio signals. - Ömer Deniz Akyildiz; Ilker Bayram (EUSIPCO, 2012)

Signal decomposition via mixed-norm regularization. - Ömer Deniz Akyildiz; Ilker Bayram (SIU, 2012)

Primal-dual algorithms for audio decomposition using mixed norms. - Ilker Bayram; Ömer Deniz Akyildiz (Signal Image Video Process., 2014)

Adaptive noisy importance sampling for stochastic optimization. - Ömer Deniz Akyildiz; Inés P. Mariño; Joaquín Míguez (CAMSAP, 2017)

The Incremental Proximal Method: A Probabilistic Perspective. - Ömer Deniz Akyildiz; Victor Elvira; Joaquín Míguez (ICASSP, 2018)

Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization. - Ying Zhang 037; Ömer Deniz Akyildiz; Theo Damoulas; Sotirios Sabanis (CoRR, 2019)

Probabilistic sequential matrix factorization. - Ömer Deniz Akyildiz; Theodoros Damoulas; Mark F. J. Steel (CoRR, 2019)

Dictionary filtering: a probabilistic approach to online matrix factorisation. - Ömer Deniz Akyildiz; Joaquín Míguez (Signal Image Video Process., 2019)

A Probabilistic Incremental Proximal Gradient Method. - Ömer Deniz Akyildiz; Emilie Chouzenoux; Víctor Elvira; Joaquín Míguez (IEEE Signal Process. Lett., 2019)

Generalised Bayesian Filtering via Sequential Monte Carlo. - Ayman Boustati; Ömer Deniz Akyildiz; Theodoros Damoulas; Adam M. Johansen (NeurIPS, 2020)

VarGrad: A Low-Variance Gradient Estimator for Variational Inference. - Lorenz Richter; Ayman Boustati; Nikolas Nüsken; Francisco J. R. Ruiz; Ömer Deniz Akyildiz (NeurIPS, 2020)

Generalized Bayesian Filtering via Sequential Monte Carlo. - Ayman Boustati; Ömer Deniz Akyildiz; Theodoros Damoulas; Adam M. Johansen (CoRR, 2020)

Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization. - Ömer Deniz Akyildiz; Dan Crisan; Joaquín Míguez (Stat. Comput., 2020)

Nudging the particle filter. - Ömer Deniz Akyildiz; Joaquín Míguez (Stat. Comput., 2020)

Probabilistic Sequential Matrix Factorization. - Ömer Deniz Akyildiz; Gerrit J. J. van den Burg; Theodoros Damoulas; Mark F. J. Steel (AISTATS, 2021)

Convergence rates for optimised adaptive importance samplers. - Ömer Deniz Akyildiz; Joaquín Míguez (Stat. Comput., 2021)

Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. - Arnaud Vadeboncoeur; Ömer Deniz Akyildiz; Ieva Kazlauskaite; Mark Girolami; Fehmi Cirak (CoRR, 2022)

$Φ$-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering. - Alex Glyn-Davies; Connor Duffin; Ömer Deniz Akyildiz; Mark Girolami (CoRR, 2022)

Statistical Finite Elements via Langevin Dynamics. - Ömer Deniz Akyildiz; Connor Duffin; Sotirios Sabanis; Mark Girolami (SIAM/ASA J. Uncertain. Quantification, 2022)

Random Grid Neural Processes for Parametric Partial Differential Equations. - Arnaud Vadeboncoeur; Ieva Kazlauskaite; Yanni Papandreou; Fehmi Cirak; Mark Girolami; Ömer Deniz Akyildiz (ICML, 2023)

Gradient-based adaptive importance samplers. - Víctor Elvira; Emilie Chouzenoux; Ömer Deniz Akyildiz; Luca Martino (J. Frankl. Inst., 2023)

Fully probabilistic deep models for forward and inverse problems in parametric PDEs. - Arnaud Vadeboncoeur; Ömer Deniz Akyildiz; Ieva Kazlauskaite; Mark Girolami; Fehmi Cirak (J. Comput. Phys., 2023)

A Primer on Variational Inference for Physics-Informed Deep Generative Modelling. - Alex Glyn-Davies; Arnaud Vadeboncoeur; Ömer Deniz Akyildiz; Ieva Kazlauskaite; Mark Girolami (CoRR, 2024)

On Diffusion Posterior Sampling via Sequential Monte Carlo for Zero-Shot Scaffolding of Protein Motifs. - James Matthew Young; Ömer Deniz Akyildiz (CoRR, 2024)

Gaussian entropic optimal transport: Schrödinger bridges and the Sinkhorn algorithm. - Ömer Deniz Akyildiz; Pierre Del Moral; Joaquín Míguez (CoRR, 2024)

Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation. - Alex Glyn-Davies; Connor Duffin; Ömer Deniz Akyildiz; Mark Girolami (J. Comput. Phys., 2024)

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. - Ömer Deniz Akyildiz; Sotirios Sabanis (J. Mach. Learn. Res., 2024)

Sequential Discretization Schemes for a Class of Stochastic Differential Equations and their Application to Bayesian Filtering. - Ömer Deniz Akyildiz; Dan Crisan; Joaquín Míguez (SIAM J. Numer. Anal., 2024)

Tweedie Moment Projected Diffusions for Inverse Problems. - Benjamin Boys; Mark Girolami; Jakiw Pidstrigach; Sebastian Reich; Alan Mosca; Ömer Deniz Akyildiz (Trans. Mach. Learn. Res., 2024)

Proximal Interacting Particle Langevin Algorithms. - Paula Cordero-Encinar; Francesca R. Crucinio; Ömer Deniz Akyildiz (UAI, 2025)

Statistical Finite Elements via Interacting Particle Langevin Dynamics. - Alex Glyn-Davies; Connor Duffin; Ieva Kazlauskaite; Mark Girolami; Ömer Deniz Akyildiz (SIAM/ASA J. Uncertain. Quantification, 2025)

Efficient Prior Calibration from Indirect Data. - Ömer Deniz Akyildiz; Mark Girolami; Andrew M. Stuart; Arnaud Vadeboncoeur (SIAM J. Sci. Comput., 2025)

A Proximal Newton Adaptive Importance Sampler. - Víctor Elvira; Émilie Chouzenoux; Ömer Deniz Akyildiz (IEEE Signal Process. Lett., 2025)

On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates. - Stefano Bruno; Ying Zhang 037; Dongyoung Lim; Ömer Deniz Akyildiz; Sotirios Sabanis (Trans. Mach. Learn. Res., 2025)

Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling. - Paula Cordero-Encinar; Ömer Deniz Akyildiz; Andrew B. Duncan (CoRR, 2025)

Nudging state-space models for Bayesian filtering under misspecified dynamics. - Fabián González; Ömer Deniz Akyildiz; Dan Crisan; Joaquín Míguez (Stat. Comput., 2025)