Preprints, published papers, slides, posters & software.

Lecture notes
  • Stochastic Simulation: From Uniform Random Numbers to Generative Models (Draft), O. D. Akyildiz, 2023 [PDF], [Jupyter Book].
Working papers
  • On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates, S. Bruno, Y. Zhang, D. Lim, O. D. Akyildiz, S. Sabanis, 2023, [arXiv].
  • Tweedie Moment Projected Diffusions For Inverse Problems, B. Boys, M. Girolami, J. Pidstrigach, S. Reich, A. Mosca, O. D. Akyildiz, 2023, [arXiv].
  • Adaptively Optimised Adaptive Importance Samplers, C. A. C. C. Perello, Ö. D. Akyildiz, 2023, [arXiv].
  • Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation, Ö. D. Akyildiz, F. R. Crucinio, M. Girolami, T. Johnston, S. Sabanis, 2023, [arXiv].
  • \(\Phi\)-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation, A. Glyn-Davies, C. Duffin, Ö. D. Akyildiz, M. Girolami, 2022, [arXiv].
Selected Publications
  • Global convergence of optimized adaptive importance samplers, Ö. D. Akyildiz, 2024 (to appear), Foundations of Data Science (FoDS), [arXiv].
  • Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization, Ö. D. Akyildiz, S. Sabanis, Journal of Machine Learning Research (JMLR), 2024 (to appear), [arXiv].
  • Sequential discretisation schemes for a class of stochastic differential equations and their application to Bayesian filtering, Ö. D. Akyildiz, D. Crisan, J. Miguez, SIAM Journal on Numerical Analysis, 2024 (to appear), [arXiv].
  • Fully probabilistic deep models for forward and inverse problems in parametric PDEs, A. Vadeboncoeur, Ö. D. Akyildiz, I. Kazlauskaite, M. Girolami, F. Cirak, Journal of Computational Physics (2023), [arXiv], [journal].
  • Gradient-based Adaptive Importance Samplers, V. Elvira, E. Chouzenoux, Ö. D. Akyildiz, L. Martino, Journal of the Franklin Institute, 2023 [arXiv], [journal].
  • Random Grid Neural Processes for Parametric Partial Differential Equations, A. Vadeboncoeur, I. Kazlauskaite, F. Cirak, M. Girolami, Ö. D. Akyildiz, International Conference of Machine Learning (ICML), 2023, [arXiv].
  • Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization, Y. Zhang, Ö. D. Akyildiz, T. Damoulas, S. Sabanis, Journal of Applied Mathematics and Optimization, 2023, [journal, arXiv].
  • Statistical Finite Elements via Langevin Dynamics, Ö. D. Akyildiz*, C. Duffin*, S. Sabanis, M. Girolami, SIAM/ASA Journal of Uncertainty Quantification, 2022 [journal].
  • Probabilistic sequential matrix factorization, Ö. D. Akyildiz*, G. J.J. van den Burg*, T. Damoulas, M. F. J. Steel, AISTATS 2021, [arXiv], [code] (*joint first authors).
  • Convergence rates for optimised adaptive importance samplers, Ö. D. Akyıldız, J. Miguez. Statistics and Computing, 31, 12 (2021), [journal] [arXiv].
  • VarGrad: A Low Variance Gradient Estimator for Variational Inference, L. Richter, A. Boustati, N. Nuesken, F. J. Ruiz, Ö. D. Akyildiz, NeurIPS 2020, [arXiv].
  • Generalized Bayesian Filtering via Sequential Monte Carlo, A. Boustati*, Ö. D. Akyildiz*, T. Damoulas, A. M. Johansen, NeurIPS 2020, [arXiv].
  • Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization, Ö. D. Akyildiz, D. Crisan, J. Miguez. Statistics and Computing, 2020, [journal].
  • Nudging the particle filter, Ö. D. Akyıldız, J. Miguez. Statistics and Computing, 2020, [journal], [arXiv], [poster].
  • A probabilistic incremental proximal gradient method, Ö. D. Akyildiz, E. Chouzenoux, V. Elvira, J. Miguez. IEEE Signal Processing Letters (to appear), [IEEExplore], [arXiv], 2019.
  • Dictionary filtering: A probabilistic approach to online matrix factorisation, Ö. D. Akyildiz, J. Miguez. Signal, Image, and Video Processing, June 2019, 13(4):737-744. [journal], [pdf].
  • The Incremental Proximal Method: A Probabilistic Perspective, Ö. D. Akyıldız, V. Elvira, J. Miguez. ICASSP, 2018, [arXiv] .
  • Adaptive noisy importance sampling for stochastic optimization, Ö. D. Akyıldız, I. P. Marino, J. Miguez. IEEE CAMSAP 2017, [pdf], [IEEExplore].
  • On the relationship between online optimizers and recursive filters, Ö. D. Akyıldız, V. Elvira, J. F. Bes, J. Miguez. NIPS Workshop on Optimizing the Optimizers, December 2016, Barcelona, Spain, [pdf], [poster].
  • A probabilistic interpretation of replicator-mutator dynamics, Ö. D. Akyıldız, December 2017, [arXiv].
  • Matrix Factorisation with Linear Filters, Ö. D. Akyıldız, September 2015, [arXiv], [discussion], [slides].
  • Online Matrix Factorisation via Broyden Updates, Ö. D. Akyıldız, June 2015, [arXiv], [MLSS poster].
  • Primal-Dual Algorithms for Audio Decomposition Using Mixed Norms, İ. Bayram and Ö. D. Akyıldız. Signal, Image and Video Processing, 8(1):95-110, January 2014. [pdf].
  • An EM Algorithm for Learning in Controlled Linear Dynamical Systems, O. D. Akyildiz, 2013, [pdf].
  • An Analysis Prior Based Decomposition Method for Audio Signals, Ö. D. Akyıldız, İ. Bayram, EUSIPCO 2012.
  • Sequential and adaptive Bayesian computation for inference and optimization, PhD thesis, Ö. D. Akyıldız, March 2019, Universidad Carlos III de Madrid, [pdf], [bibtex], [slides].