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2020 – today
- 2023
[i12]Antti Koskela, Tejas Kulkarni:
Practical Differentially Private Hyperparameter Tuning with Subsampling. CoRR abs/2301.11989 (2023)- 2022
- [i11]Antti Koskela, Marlon Tobaben, Antti Honkela:
Individual Privacy Accounting with Gaussian Differential Privacy. CoRR abs/2209.15596 (2022) - 2021
- [j5]Peter Kandolf
, Antti Koskela, Samuel D. Relton, Marcel Schweitzer:
Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods. Numer. Linear Algebra Appl. 28(6) (2021) - [c5]Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela:
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT. AISTATS 2021: 3358-3366 - [c4]Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela:
Differentially Private Bayesian Inference for Generalized Linear Models. ICML 2021: 5838-5849 - [i10]Antti Koskela, Antti Honkela:
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT. CoRR abs/2102.12412 (2021) - [i9]Antti Koskela, Mikko A. Heikkilä, Antti Honkela:
Tight Accounting in the Shuffle Model of Differential Privacy. CoRR abs/2106.00477 (2021) - [i8]Ossi Räisä, Antti Koskela, Antti Honkela:
Differentially Private Hamiltonian Monte Carlo. CoRR abs/2106.09376 (2021) - 2020
- [c3]Antti Koskela, Antti Honkela:
Learning Rate Adaptation for Differentially Private Learning. AISTATS 2020: 2465-2475 - [c2]Antti Koskela, Joonas Jälkö, Antti Honkela:
Computing Tight Differential Privacy Guarantees Using FFT. AISTATS 2020: 2560-2569 - [i7]Antti Koskela, Samuel D. Relton:
Sampling of Stochastic Differential Equations using the Karhunen-Loève Expansion and Matrix Functions. CoRR abs/2004.05687 (2020) - [i6]Antti Koskela, Joonas Jälkö, Lukas Prediger, Antti Honkela:
Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT. CoRR abs/2006.07134 (2020) - [i5]Mikko A. Heikkilä, Antti Koskela, Kana Shimizu, Samuel Kaski, Antti Honkela:
Differentially private cross-silo federated learning. CoRR abs/2007.05553 (2020) - [i4]Peter Kandolf, Antti Koskela, Samuel D. Relton, Marcel Schweitzer:
Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods. CoRR abs/2008.12926 (2020) - [i3]Tejas Kulkarni, Joonas Jälkö, Antti Koskela, Samuel Kaski, Antti Honkela:
Differentially Private Bayesian Inference for Generalized Linear Models. CoRR abs/2011.00467 (2020)
2010 – 2019
- 2019
- [i2]Antti Koskela, Joonas Jälkö, Antti Honkela:
Computing Exact Guarantees for Differential Privacy. CoRR abs/1906.03049 (2019) - 2018
- [j4]Elias Jarlebring, Antti Koskela, Giampaolo Mele:
Disguised and new quasi-Newton methods for nonlinear eigenvalue problems. Numer. Algorithms 79(1): 311-335 (2018) - [i1]Antti Koskela, Antti Honkela:
Learning rate adaptation for differentially private stochastic gradient descent. CoRR abs/1809.03832 (2018) - 2016
- [j3]Antti Koskela, Elias Jarlebring, Michiel E. Hochstenbach:
Krylov Approximation of Linear ODEs with Polynomial Parameterization. SIAM J. Matrix Anal. Appl. 37(2): 519-538 (2016) - 2014
- [j2]Antti Koskela, Alexander Ostermann:
A Moment-Matching Arnoldi Iteration for Linear Combinations of φ Functions. SIAM J. Matrix Anal. Appl. 35(4): 1344-1363 (2014) - 2013
- [j1]Antti Koskela, Alexander Ostermann:
Exponential Taylor methods: Analysis and implementation. Comput. Math. Appl. 65(3): 487-499 (2013) - [c1]Antti Koskela:
Approximating the Matrix Exponential of an Advection-Diffusion Operator Using the Incomplete Orthogonalization Method. ENUMATH 2013: 345-353
Coauthor Index
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