Faculty
Dr. Sebastian Stich ist Tenured Faculty am CISPA Helmholtz-Zentrum für Informationssicherheit. Seit Juni 2020 ist er Mitglied des European Lab for Learning and Intelligent Systems. Vom 1. Dezember 2016 bis zum 30. November 2021 war er Forscher an der EPFL unter der Leitung von Prof. Martin Jaggi im Machine Learning and Optimization Laboratory (MLO). Vom 1. November 2014 bis 31. Oktober 2016 arbeitete er mit Prof. Yurii Nesterov und Prof. François Glineur am Center for Operations Research and Econometrics (CORE) und am ICTEAM. Vom 15. September 2010 bis 30. September 2014 war er Doktorand in der Forschungsgruppe von Prof. Emo Welzl, betreut von Prof. Bernd Gärtner und Christian Lorenz Müller. Von September 2005 bis März 2010 absolvierte er seinen Bachelor und Master in Mathematik an der ETH Zürich.
Conference on Neural Information Processing Systems (NeurIPS) Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
Transactions on Machine Learning Research Personalized Federated Learning via Low-Rank Matrix Optimization
International Conference on Machine Learning-Workshop (ICML-W) Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts
International Conference on Machine Learning (ICML)
International Conference on Machine Learning (ICML) Decoupled SGDA for Games with Intermittent Strategy Communication
International Conference on Artificial Intelligence and Statistics (AISTATS) Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
International Conference on Learning Representations-Workshop (ICLR-W) LoRAM: Low-Rank Adaptation of Large Language Models on Manifold
International Conference on Learning Representations (ICLR) Towards Faster Decentralized Stochastic Optimization with Communication Compression
International Conference on Learning Representations (ICLR) Scalable Decentralized Learning with Teleportation
Conference on Neural Information Processing Systems (NeurIPS) Stabilized Proximal Point Methods for Federated Optimization