| Computing Tight Differential Privacy Guarantees Using FFT A Koskela, J Jälkö, A Honkela International Conference on Artificial Intelligence and Statistics, 2560-2569, 2020 | 72 | 2020 |
| Ethylene glycol revisited: Molecular dynamics simulations and visualization of the liquid and its hydrogen-bond network A Kaiser, O Ismailova, A Koskela, SE Huber, M Ritter, B Cosenza, ... Journal of Molecular Liquids 189, 20-29, 2014 | 69 | 2014 |
| Tight differential privacy for discrete-valued mechanisms and for the subsampled gaussian mechanism using FFT A Koskela, J Jälkö, L Prediger, A Honkela International Conference on Artificial Intelligence and Statistics, 3358-3366, 2021 | 42* | 2021 |
| Learning rate adaptation for differentially private learning A Koskela, A Honkela International Conference on Artificial Intelligence and Statistics, 2465-2475, 2020 | 40* | 2020 |
| Differentially private cross-silo federated learning MA Heikkilä, A Koskela, K Shimizu, S Kaski, A Honkela arXiv preprint arXiv:2007.05553, 2020 | 27 | 2020 |
| Exponential Taylor methods: Analysis and implementation A Koskela, A Ostermann Computers & Mathematics with Applications 65 (3), 487-499, 2013 | 25 | 2013 |
| Differentially private Bayesian inference for generalized linear models T Kulkarni, J Jälkö, A Koskela, S Kaski, A Honkela International Conference on Machine Learning, 5838-5849, 2021 | 24 | 2021 |
| Splitting methods for time integration of trajectories in combined electric and magnetic fields C Knapp, A Kendl, A Koskela, A Ostermann Physical Review E 92 (6), 063310, 2015 | 20 | 2015 |
| Analysis of Krylov Subspace Approximation to Large Scale Differential Riccati Equations A Koskela, H Mena Electronic Transactions on Numerical Analysis 52, 431--454, 2020 | 19* | 2020 |
| Approximating the matrix exponential of an advection-diffusion operator using the incomplete orthogonalization method A Koskela Numerical Mathematics and Advanced Applications-ENUMATH 2013: Proceedings of …, 2014 | 15 | 2014 |
| Numerical Accounting in the Shuffle Model of Differential Privacy A Koskela, M Heikkilä, A Honkela Transactions on Machine Learning Research, 2023 | 14* | 2023 |
| Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods P Kandolf, A Koskela, SD Relton, M Schweitzer Numerical Linear Algebra with Applications, e2401, 2021 | 12 | 2021 |
| Computing differential privacy guarantees for heterogeneous compositions using FFT A Koskela, A Honkela arXiv preprint arXiv:2102.12412, 2021 | 11 | 2021 |
| Disguised and new quasi-Newton methods for nonlinear eigenvalue problems E Jarlebring, A Koskela, G Mele Numerical Algorithms 79, 311-335, 2018 | 10 | 2018 |
| Individual Privacy Accounting with Gaussian Differential Privacy A Koskela, M Tobaben, A Honkela International Conference on Learning Representations, 2023 | 7 | 2023 |
| Differentially private hamiltonian monte carlo O Räisä, A Koskela, A Honkela arXiv preprint arXiv:2106.09376, 2021 | 5 | 2021 |
| Krylov integrators for Hamiltonian systems T Eirola, A Koskela BIT Numerical Mathematics 59, 57-76, 2019 | 5 | 2019 |
| The infinite Arnoldi exponential integrator for linear inhomogeneous ODEs A Koskela, E Jarlebring arXiv preprint arXiv:1502.01613, 2015 | 5 | 2015 |
| A Moment-Matching Arnoldi Iteration for Linear Combinations of Functions A Koskela, A Ostermann SIAM Journal on Matrix Analysis and Applications 35 (4), 1344-1363, 2014 | 4 | 2014 |
| Krylov approximation of linear ODEs with polynomial parameterization A Koskela, E Jarlebring, ME Hochstenbach SIAM Journal on Matrix Analysis and Applications 37 (2), 519-538, 2016 | 3 | 2016 |
