Approximating the Matrix Exponential of an Advection-Diffusion Operator Using the Incomplete Orthogonalization Method. - Antti Koskela (ENUMATH, 2013)

Exponential Taylor methods: Analysis and implementation. - Antti Koskela; Alexander Ostermann (Comput. Math. Appl., 2013)

A Moment-Matching Arnoldi Iteration for Linear Combinations of φ Functions. - Antti Koskela; Alexander Ostermann (SIAM J. Matrix Anal. Appl., 2014)

Krylov Approximation of Linear ODEs with Polynomial Parameterization. - Antti Koskela; Elias Jarlebring; Michiel E. Hochstenbach (SIAM J. Matrix Anal. Appl., 2016)

Learning rate adaptation for differentially private stochastic gradient descent. - Antti Koskela; Antti Honkela (CoRR, 2018)

Disguised and new quasi-Newton methods for nonlinear eigenvalue problems. - Elias Jarlebring; Antti Koskela; Giampaolo Mele (Numer. Algorithms, 2018)

Computing Exact Guarantees for Differential Privacy. - Antti Koskela; Joonas Jälkö; Antti Honkela (CoRR, 2019)

Learning Rate Adaptation for Differentially Private Learning. - Antti Koskela; Antti Honkela (AISTATS, 2020)

Computing Tight Differential Privacy Guarantees Using FFT. - Antti Koskela; Joonas Jälkö; Antti Honkela (AISTATS, 2020)

Sampling of Stochastic Differential Equations using the Karhunen-Loève Expansion and Matrix Functions. - Antti Koskela; Samuel D. Relton (CoRR, 2020)

Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT. - Antti Koskela; Joonas Jälkö; Lukas Prediger; Antti Honkela (CoRR, 2020)

Differentially private cross-silo federated learning. - Mikko A. Heikkilä; Antti Koskela; Kana Shimizu; Samuel Kaski; Antti Honkela (CoRR, 2020)

Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. - Antti Koskela; Joonas Jälkö; Lukas Prediger; Antti Honkela (AISTATS, 2021)

Differentially Private Bayesian Inference for Generalized Linear Models. - Tejas Kulkarni; Joonas Jälkö; Antti Koskela; Samuel Kaski; Antti Honkela (ICML, 2021)

Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT. - Antti Koskela; Antti Honkela (CoRR, 2021)

Tight Accounting in the Shuffle Model of Differential Privacy. - Antti Koskela; Mikko A. Heikkilä; Antti Honkela (CoRR, 2021)

Differentially Private Hamiltonian Monte Carlo. - Ossi Räisä; Antti Koskela; Antti Honkela (CoRR, 2021)

Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods. - Peter Kandolf; Antti Koskela; Samuel D. Relton; Marcel Schweitzer (Numer. Linear Algebra Appl., 2021)

Individual Privacy Accounting with Gaussian Differential Privacy. - Antti Koskela; Marlon Tobaben; Antti Honkela (ICLR, 2023)

Practical Differentially Private Hyperparameter Tuning with Subsampling. - Antti Koskela; Tejas D. Kulkarni (NeurIPS, 2023)

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. - Rachel Redberg; Antti Koskela; Yu-Xiang Wang 003 (NeurIPS, 2023)

Practical Differentially Private Hyperparameter Tuning with Subsampling. - Antti Koskela; Tejas Kulkarni (CoRR, 2023)

Numerical Accounting in the Shuffle Model of Differential Privacy. - Antti Koskela; Mikko A. Heikkilä; Antti Honkela (Trans. Mach. Learn. Res., 2023)

Privacy Profiles for Private Selection. - Antti Koskela; Rachel Redberg; Yu-Xiang Wang 003 (ICML, 2024)

Black Box Differential Privacy Auditing Using Total Variation Distance. - Antti Koskela; Jafar Mohammadi (CoRR, 2024)

Differentially Private Convex Approximation of Two-Layer ReLU Networks. - Antti Koskela (CoRR, 2024)

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning. - Dong Chen; Alice Dethise; Istemi Ekin Akkus; Ivica Rimac; Klaus Satzke; Antti Koskela; Marco Canini; Wei Wang 030; Ruichuan Chen (CoRR, 2024)

Auditing Differential Privacy Guarantees Using Density Estimation. - Antti Koskela; Jafar Mohammadi (SaTML, 2025)

Communication Efficient Differentially Private Federated Learning Using Second Order Information. - Mounssif Krouka; Antti Koskela; Tejas D. Kulkarni (Proc. Priv. Enhancing Technol., 2025)

On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models. - Antti Koskela; Mohamed Seif; Andrea J. Goldsmith (CoRR, 2025)

Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Models. - Antti Koskela; Tejas D. Kulkarni (CoRR, 2025)