Preprints, published papers, slides, posters & software.

Working papers
  • Gradient-based Adaptive Importance Samplers, V. Elvira, E. Chouzenoux, Ö. D. Akyildiz, L. Martino, 2022, [arXiv].
  • \(\Phi\)-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering, A. Glyn-Davies, C. Duffin, Ö. D. Akyildiz, M. Girolami, 2022, [arXiv].
  • Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs, A. Vadeboncoeur, Ö. D. Akyildiz, I. Kazlauskaite, M. Girolami, F. Cirak, 2022, [arXiv].
  • Space-sequential particle filters for high-dimensional dynamical systems described by stochastic differential equations, Ö. D. Akyildiz, D. Crisan, J. Miguez, 2022, [arXiv].
  • Global convergence of optimized adaptive importance samplers, Ö. D. Akyildiz, 2022, [arXiv].
  • Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization, Ö. D. Akyildiz, S. Sabanis, 2020, [arXiv].
Selected Publications
  • 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 (to appear), 2022, [arXiv].
  • Statistical Finite Elements via Langevin Dynamics, Ö. D. Akyildiz*, C. Duffin*, S. Sabanis, M. Girolami, SIAM/ASA Journal of Uncertainty Quantification (to appear), 2022 [arXiv].
  • 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 (to appear), 2019, [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].
  • 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].
Others
  • 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.
Thesis
  • Sequential and adaptive Bayesian computation for inference and optimization, PhD thesis, Ö. D. Akyıldız, March 2019, Universidad Carlos III de Madrid, [pdf], [bibtex].