Antti Koskela

According to our database1, Antti Koskela authored at least 24 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Privacy Profiles for Private Selection.
CoRR, 2024

2023
Numerical Accounting in the Shuffle Model of Differential Privacy.
Trans. Mach. Learn. Res., 2023

Practical Differentially Private Hyperparameter Tuning with Subsampling.
CoRR, 2023

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Practical Differentially Private Hyperparameter Tuning with Subsampling.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Individual Privacy Accounting with Gaussian Differential Privacy.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021
Computing low-rank approximations of the Fréchet derivative of a matrix function using Krylov subspace methods.
Numer. Linear Algebra Appl., 2021

Differentially Private Hamiltonian Monte Carlo.
CoRR, 2021

Tight Accounting in the Shuffle Model of Differential Privacy.
CoRR, 2021

Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT.
CoRR, 2021

Differentially Private Bayesian Inference for Generalized Linear Models.
Proceedings of the 38th International Conference on Machine Learning, 2021

Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Differentially private cross-silo federated learning.
CoRR, 2020

Tight Approximate Differential Privacy for Discrete-Valued Mechanisms Using FFT.
CoRR, 2020

Sampling of Stochastic Differential Equations using the Karhunen-Loève Expansion and Matrix Functions.
CoRR, 2020

Computing Tight Differential Privacy Guarantees Using FFT.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Learning Rate Adaptation for Differentially Private Learning.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Computing Exact Guarantees for Differential Privacy.
CoRR, 2019

2018
Disguised and new quasi-Newton methods for nonlinear eigenvalue problems.
Numer. Algorithms, 2018

Learning rate adaptation for differentially private stochastic gradient descent.
CoRR, 2018

2016
Krylov Approximation of Linear ODEs with Polynomial Parameterization.
SIAM J. Matrix Anal. Appl., 2016

2014
A Moment-Matching Arnoldi Iteration for Linear Combinations of φ Functions.
SIAM J. Matrix Anal. Appl., 2014

2013
Exponential Taylor methods: Analysis and implementation.
Comput. Math. Appl., 2013

Approximating the Matrix Exponential of an Advection-Diffusion Operator Using the Incomplete Orthogonalization Method.
Proceedings of the Numerical Mathematics and Advanced Applications - ENUMATH 2013, 2013


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