Publications

My publications have been grouped into books, journal papers, papers in conferences and workshops, theses, and miscellaneous other reports and written works. Current working papers and unpublished pre-prints are listed at the bottom.

Book

Applied Stochastic Differential Equations

By Simo Särkkä and Arno Solin

Published by Cambridge University Press in 2019.

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge–Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book’s practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-driven derivations and computational assignments. MATLAB/Octave source code is available for download, promoting hands-on work with the methods.

With permission from the publisher, we are providing a PDF version of the book here. This PDF version is made available for personal use. The copyright in all material rests with the authors (Simo Särkkä and Arno Solin). Commercial reproduction is prohibited, except as authorised by the author and publisher.

Journal Articles

Arno Solin and Simo Särkkä (2020). Hilbert Space Methods for Reduced-Rank Gaussian Process Regression. Statistics and Computing, 30(2):419–446.

James Hensman, Nicolas Durrande, and Arno Solin (2018). Variational Fourier features for Gaussian processes. Journal of Machine Learning Research (JMLR), 18(151):1–52.

Arno Solin, Manon Kok, Niklas Wahlström, Thomas B. Schön, and Simo Särkkä (2018). Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes. IEEE Transactions on Robotics (T-RO), 34(4):1112–1127.

Juho Kokkala, Arno Solin, and Simo Särkkä (2016). Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems. Journal of Advances in Information Fusion, 11(1):15–30.

Arno Solin and Simo Särkkä (2013). Infinite-dimensional Bayesian filtering for detection of quasiperiodic phenomena in spatiotemporal data. Physical Review E, 88(5):052909.

Simo Särkkä, Arno Solin, and Jouni Hartikainen (2013). Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing. IEEE Signal Processing Magazine, 30(4):51–61. IEEE.

Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin (2013). Dynamic Retrospective Filtering of Physiological Noise in BOLD fMRI: DRIFTER. NeuroImage, 60(2):1517–1527.

Conferences

Including symposia and workshops.

2021

Will Tebbutt, Arno Solin, and Richard E. Turner (2021). Combining pseudo-point and state space approximations for sum-separable Gaussian processes. Accepted for publication in Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI).

William J. Wilkinson, Arno Solin, and Vincent Adam (2021). Sparse algorithms for Markovian Gaussian processes. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 130:1747–1755.

Will Tebbutt, Arno Solin, and Richard E. Turner (2021). Combining pseudo-point and state space approximations for sum-separable Gaussian processes. In 3rd Symposium on Advances in Approximate Bayesian Inference (AABI).

Yuxin Hou, Ari Heljakka, and Arno Solin (2021). Gaussian process priors for view-aware inference. In Thirty-fifth AAAI Conference on Artificial Intelligence (AAAI). 35(9):7762–7770. AAAI Press.

Yuxin Hou, Arno Solin, and Juho Kannala (2021). Novel view synthesis via depth-guided skip connections. In IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 3118–3127. Waikoloa, HI, USA. IEEE.

2020

Lassi Meronen, Christabella Irwanto, and Arno Solin (2020). Stationary activations for uncertainty calibration in deep learning. In Advances in Neural Information Processing Systems (NeurIPS). Pages 2338–2350.

Ari Heljakka, Yuxin Hou, Juho Kannala, and Arno Solin (2020). Deep automodulators. In Advances in Neural Information Processing Systems (NeurIPS). Pages 13702–13713.

Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala, and Arno Solin (2020). Movement-induced priors for deep stereo. In International Conference on Pattern Recognition (ICPR).

William J. Wilkinson, Paul Chang, Michael Riis Andersen, and Arno Solin (2020). State space expectation propagation: Efficient inference schemes for temporal Gaussian processes. In Proceedings of the 37th International Conference on Machine Learning (ICML). PMLR 119:10270-10281.

Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, and Richard E. Turner (2020). Scalable exact inference in multi-output Gaussian processes. In Proceedings of the 37th International Conference on Machine Learning (ICML). PMLR 119:1190-1201.

Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, and Arno Solin (2020). Fast variational learning in state-space Gaussian process models. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

Lassi Meronen, William J. Wilkinson, and Arno Solin (2020). Movement tracking by optical flow assisted inertial navigation. In Proceedings of the International Conference on Information Fusion (FUSION).

Ari Heljakka, Arno Solin, and Juho Kannala (2020). Towards photographic image manipulation with balanced growing of generative autoencoders. In IEEE Winter Conference on Applications of Computer Vision (WACV). Aspen, CO, USA.

2019

William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, and Arno Solin (2019). Global approximate inference via local linearisation for temporal Gaussian processes. In 2nd Symposium on Advances in Approximate Bayesian Inference (AABI). Vancouver, Canada.

Ari Heljakka, Yuxin Hou, Juho Kannala, and Arno Solin (2019). Conditional image sampling by deep automodulators. In Bayesian Deep Learning Workshop, NeurIPS 2019 Workshop. Vancouver, Canada.

Yuxin Hou, Juho Kannala, and Arno Solin (2019). Multi-view stereo by temporal nonparametric fusion. In International Conference on Computer Vision (ICCV). Pages 2651–2660. Seoul, Korea.

Santiago Cortés, Yuxin Hou, Juho Kannala, and Arno Solin (2019). Iterative path reconstruction for large-scale inertial navigation on smartphones. In Proceedings of the International Conference on Information Fusion (FUSION). Ottawa, Canada.

William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, and Arno Solin (2019). End-to-End Probabilistic Inference for Nonstationary Audio Analysis. In Proceedings of the 36th International Conference on Machine Learning (ICML). PMLR 97:6776–6785. Long Beach, California, USA.

Yuxin Hou, Arno Solin, and Juho Kannala (2019). Unstructured multi-view depth estimation using mask-based multiplane representation. In Scandinavian Conference on Image Analysis (SCIA). Norrköping, Sweden.

William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, and Arno Solin (2019). Unifying probabilistic models for time-frequency analysis. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). Pages 3352-3356. Brighton, UK.

Arno Solin and Manon Kok (2019). Know your boundaries: Constraining Gaussian processes by variational harmonic features. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 89:2193–2202. Naha, Okinawa, Japan.

2018

Arno Solin, James Hensman, and Richard E. Turner (2018). Infinite-horizon Gaussian processes. In Advances in Neural Information Processing Systems (NeurIPS). Pages 3490–3499. Montréal, Canada.

Ari Heljakka, Arno Solin, and Juho Kannala (2018). Pioneer networks: Progressively growing generative autoencoder. In Asian Conference on Computer Vision (ACCV). Lecture Notes in Computer Science 11361:22–38. Perth, Australia. Springer.

Santiago Cortés, Arno Solin, and Juho Kannala (2018). Deep learning based speed estimation for constraining strapdown inertial navigation on smartphones. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Aalborg, Denmark.

Ari Heljakka, Arno Solin, and Juho Kannala (2018). Recursive chaining of reversible image-to-image translators for face aging. In Proceedings of Advanced Concepts for Intelligent Vision Systems (ACIVS). Lecture Notes in Computer Science 11182:309–320. Poitiers, France. Springer.

Santiago Cortés, Arno Solin, Esa Rahtu, and Juho Kannala (2018). ADVIO: An authentic dataset for visual-inertial odometry. In Proceedings of European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science 11214:425–440. Munich, Germany. Springer.

Hannes Nickisch, Arno Solin, and Alexander Grigorievskiy (2018). State Space Gaussian Processes with Non-Gaussian Likelihood. In International Conference on Machine Learning (ICML). PMLR 80:3789–3798. Stockholm, Sweden.

Arno Solin, Santiago Cortés, Esa Rahtu, and Juho Kannala (2018). Inertial Odometry on Handheld Smartphones. In Proceedings of the International Conference on Information Fusion (FUSION). Pages 1361–1368. Cambridge, UK.

Manon Kok and Arno Solin (2018). Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps. In Proceedings of the International Conference on Information Fusion (FUSION). Pages 1353–1360. Cambridge, UK.

Winner of the Best Paper Award at FUSION 2018!

Santiago Cortés, Arno Solin, and Juho Kannala (2018). Robust Gyroscope-Aided Camera Self-Calibration. In Proceedings of the International Conference on Information Fusion (FUSION). Pages 772–779. Cambridge, UK.

Arno Solin, Santiago Cortés, Esa Rahtu, and Juho Kannala (2018). PIVO: Probabilistic inertial-visual odometry for occlusion-robust navigation. In IEEE Winter Conference on Applications of Computer Vision (WACV). Pages 616–625. Lake Tahoe, NV, USA.

Eric Malmi, Aristides Gionis, and Arno Solin (2018). Computationally inferred genealogical networks uncover long-term trends in assortative mating. In Proceedings of the International World Wide Web Conference (WWW). Pages 883–892. Lyon, France.

Winner of the Best Paper Award at Young Demographers Conference!

2017

Toni Karvonen, Arno Solin, Ángel F. García-Fernández, Filip Tronarp, Simo Särkkä, and Fa-Hsuan Lin (2017). Where is physiological noise lurking in k-space?. In Proceedings of ISMRM 2017, 25th Annual Meeting & Exhibition. Honolulu, HI, USA. Number 5300. The International Society for Magnetic Resonance in Medicine. Appeared as abstract and e-poster.

2016

Arno Solin, Simo Särkkä, Juho Kannala, and Esa Rahtu (2016). Terrain navigation in the magnetic landscape: Particle filtering for indoor positioning. In Proceedings of the European Navigation Conference (ENC). Pages 1–9. IEEE.

Andreas Svensson, Arno Solin, Simo Särkkä, and Thomas B. Schön (2016). Computationally efficient Bayesian learning of Gaussian process state space models. In Proceedings of the Nineteenth International Conference on Artifcial Intelligence and Statistics (AISTATS). JMLR W&CP 51:213–221. Cadiz, Spain.

2015

Andreas Svensson, Thomas B. Schön, Arno Solin, and Simo Särkkä (2015). Nonlinear state space model identification using a regularized basis function expansion. In Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Pages 481–484. IEEE.

Eric Malmi, Arno Solin, and Aristides Gionis (2015). The blind leading the blind: Network-based location estimation under uncertainty. In Machine Learning and Knowledge Discovery in Databases (ECML PKDD). Lecture Notes in Computer Science 9285:406–421. Springer.

Eric Malmi, Arno Solin, and Aristides Gionis (2015). Reconstructing and analyzing family trees: Towards longitudinal computational social science. In International Conference on Computational Social Science (ICCSS). Appeared as abstract and poster.

Arno Solin and Simo Särkkä (2015). State space methods for efficient inference in Student-t process regression. In Proceedings of the Eighteenth International Conference on Artifcial Intelligence and Statistics (AISTATS). JMLR W&CP 38:885–893. San Diego, US.

2014

Arno Solin and Simo Särkkä (2014). Gaussian quadratures for state space approximation of scale mixtures of squared exponential covariance functions. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Reims, France.

Juho Kokkala, Arno Solin, and Simo Särkkä (2014). Expectation maximization based parameter estimation by sigma-point and particle smoothing. In Proceedings of the 17th International Conference on Information Fusion (FUSION). Salamanca, Spain.

Arno Solin and Simo Särkkä (2014). Explicit link between periodic covariance functions and state space models. In Proceedings of the Seventeenth International Conference on Artifcial Intelligence and Statistics (AISTATS). JMLR W&CP 33:904–912. Reykjavik, Iceland.

Arno Solin, Simo Särkkä, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, and Fa-Hsuan Lin (2014). Catching physiological noise: Comparison of DRIFTER in image and k-Space. In Proceedings of ISMRM 2014, 22nd Annual Meeting & Exhibition. Milan, Italy. The International Society for Magnetic Resonance in Medicine. Number 3068. Appeared as abstract and poster.

2013

Simo Särkkä and Arno Solin (2013). Continuous-space Gaussian process regression and generalized Wiener filtering with application to learning curves. In Scandinavian Conference on Image Analysis (SCIA). Lecture Notes in Computer Science 7944:172–181. Springer.

Arno Solin, Enrico Glerean, and Simo Särkkä (2013). Time–frequency dynamics of brain connectivity by stochastic oscillator models and Kalman filtering. In The 19th Annual Meeting of the Organization for Human Brain Mapping. Seattle, WA, USA. Number 1877. Appeared as abstract and poster.

Arno Solin, Simo Särkkä, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin (2013). Volumetric space-time structure of physiological noise in BOLD fMRI. In Proceedings of ISMRM 2013, 21st Annual Meeting & Exhibition. Salt Lake City, UT, US. The International Society for Magnetic Resonance in Medicine. Number 3353. Appeared as abstract and e-poster.

2012

Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, and Fa-Hsuan Lin (2012). Identification of spatio-temporal oscillatory signal structure in cerebral hemodynamics using DRIFTER. In Proceedings of ISMRM 2012, 20th Annual Meeting & Exhibition. Melbourne. The International Society for Magnetic Resonance in Medicine. Number 2842. Appeared as abstract and e-poster.

Simo Särkkä and Arno Solin (2012). On continuous-discrete cubature Kalman filtering. In Proceedings of SYSID 2012, 16th IFAC Symposium on System Identification. Pages 1210–1215. Brussels.

2011

Simo Särkkä, Aapo Nummenmaa, Arno Solin, Aki Vehtari, Thomas Witzel, Toni Auranen, Simo Vanni, Matti S. Hämälainen, and Fa-Hsuan Lin (2011). Dynamical statistical modeling of physiological noise for fast BOLD fMRI. In Proceedings of ISMRM 2011, 19th Annual Meeting & Exhibition. Montreal. The International Society for Magnetic Resonance in Medicine. Number 3592. Appeared as abstract and e-poster.

Theses

Arno Solin (2016). Stochastic Differential Equation Methods for Spatio-Temporal Gaussian Process Regression. Doctoral dissertation. Aalto University, Helsinki, Finland.

Arno Solin (2012). Hilbert Space Methods in Infinite-Dimensional Kalman Filtering. Master's thesis. School of Science, Aalto University.

Arno Solin (2010). Cubature Integration Methods in Non-Linear Kalman Filtering and Smoothing. Bachelor's thesis. Faculty of information and natural sciences, Aalto University.

Miscellaneous

Arno Solin (2012). Tracking and Elimination of Periodic Noise in fMRI Using Bayesian Inference. Semester project undertaken as course Mat-2.4108. Instructor Dr. Simo Särkkä, supervisor Prof. Harri Ehtamo. Department of Mathematics and Systems Analysis, Aalto University.

Arno Solin, Michail Katsigiannis, Kaisa Parkkila, and Bahare Torabihaghighi (2011). Modeling long-term electricity prices. Course work, Department of Mathematics and Systems Analysis, Aalto University. Undertaken as the course Mat-2.4177 in collaboration with Danske Markets, supervisor Prof. Ahti Salo.

Eric Malmi, Jussi Sainio, and Arno Solin (2010). Natural Bayesian killers. In the one-weekend mathematical competition Mathematical Contest in Modeling by COMAP (SIAM/NSA/INFORMS).

Awarded ‘Meritorious Winner’.

Arno Solin (2005). Etäisyysmittalaite, joka kartoittaa ympärillään olevaa tilaa lasermittauksen avulla ja luo tietokoneelle kolmiulotteisen mallin. Päättötyö (final project in upper secondary high-school, in Finnish). Ohjaajina Stefan Johansson (Åbo Akademin hiukkaskiihdytinlaboratorio) ja Marjo Lahti. Turun Steiner-koulun lukio, Turku.

Submitted

Including technical reports.

Rinu Boney, Jussi Sainio, Mikko Kaivola, Arno Solin, and Juho Kannala. RealAnt: An open-source low-cost quadruped for research in real-world reinforcement learning. Submitted.

Perttu Hämäläinen and Arno Solin. Deep Residual Mixture Models. Submitted.

Gabriel Riutort-Mayol, Paul-Christian Bürkner, Michael R. Andersen, Arno Solin, and Aki Vehtari. Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. Submitted.

Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, and Simo Särkkä. Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions. Submitted.

Author conflicts from within the past three years (2018–2021)

This list is automatically generated from the published papers listed above.
Alexander Grigorievskiy, Ari Heljakka, Aristides Gionis, Arno Solin, Christabella Irwanto, Dan Stowell, Eric Malmi, Eric Perim, Esa Rahtu, Hannes Nickisch, J. Scott Hosking, James Hensman, Joshua D. Reiss, Juho Kannala, Lassi Meronen, Manon Kok, Michael Riis Andersen, Mohammad Emtiyaz Khan, Muhammad Kamran Janjua, Nicolas Durrande, Niklas Wahlström, Paul Chang, Paul E. Chang, Richard E. Turner, Santiago Cortés, Simo Särkkä, Thomas B. Schön, Vincent Adam, Wessel P. Bruinsma, Will Tebbutt, William J. Wilkinson, and Yuxin Hou.