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Research Group

Stich

Optimization is at the heart of many machine learning algorithms. The focus of our research group is on fundamental research on optimization techniques for training machine learning models. The research topics include decentralized methods (collaborative learning, federated learning), but also adaptive stochastic methods, generalization performance, deep theory of deep learning, and privacy and security in machine learning.

Head of Group

Sebastian Stich

Email

Phone

Address

Stuhlsatzenhaus 5
66123 Saarbrücken (Germany)

Members

Yasin Esfandiari
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Harsha Nelaturu
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Xiaowen Jiang
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Mriganka Nath
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Ali Zindari
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Md Sakhawat Hosain

Most Recent Publications

Year 2022

2022-12

Preserving privacy with PATE for heterogeneous data

Conference / Medium

NeurIPS-W
openreviewNeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications

Tags
Algorithmic Foundations and Cryptography
Authors
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2022-11-28

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities

Conference / Medium

NeurIPS
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)NeurIPS

Tags
Algorithmic Foundations and Cryptography Trustworthy Information Processing
Authors
  • Aleksandr Beznosikov
  • Pavel Dvurechensky
  • Anastasia Koloskova
  • Valentin Samokhin
  • Sebastian Stich
  • Alexander Gasnikov
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2022-11-28

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning

Conference / Medium

NeurIPS
Advances in Neural Information Processing Systems 35 (NeurIPS 2022)NeurIPS

Tags
Algorithmic Foundations and Cryptography Trustworthy Information Processing
Authors
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2022-07-19

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

Conference / Medium

ICML
Proceedings of the 39th International Conference on Machine LearningInternational Conference on Machine Learning (ICML)

Tags
Algorithmic Foundations and Cryptography Trustworthy Information Processing
Authors
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2022-07-19

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

Conference / Medium

ICML
Proceedings of the 39th International Conference on Machine LearningInternational Conference on Machine Learning (ICML)

Tags
Algorithmic Foundations and Cryptography Trustworthy Information Processing
Authors
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