Faculty
Since December 1st 2021 Dr. Sebastian Stich is a tenure track faculty at CISPA. Since June 2020 he is a member of the European Lab for Learning and Intelligent Systems. From December 1st 2016 to November 30th 2021, he worked as a research scientist at EPFL, hosted by Prof. Martin Jaggi, Machine Learning and Optimization Laboratory (MLO). From November 1st 2014 to October 31st 2016, he worked with Prof. Yurii Nesterov and Prof. François Glineur at the Center for Operations Research and Econometrics (CORE) and the ICTEAM. From September 15th 2010 to September 30th 2014, he was a PHD student in Prof. Emo Welzl's research group, supervised by Prof. Bernd Gärtner and Christian Lorenz Müller. And from September 2005 to March 2010 he did his Bachelor and Master in Mathematics at ETH Zurich.
International Conference on Machine Learning (ICML)
Non-Convex Stochastic Composite Optimization with Polyak Momentum
International Conference on Machine Learning (ICML)
Federated Optimization with Doubly Regularized Drift Correction
International Conference on Machine Learning (ICML)
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
International Conference on Machine Learning (ICML)
On Convergence of Incremental Gradient for Non-convex Smooth Functions
Conference on Learning Theory (COLT)
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
International Conference on Learning Representations (ICLR)
EControl: Fast Distributed Optimization with Compression and Error Control.
International Conference on Learning Representations (ICLR)
An improved analysis of per-sample and per-update clipping in federated learning
International Conference on Learning Representations (ICLR)
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
Optimization Methods and Software Decentralized gradient tracking with local steps
Conference on Neural Information Processing Systems (NeurIPS)
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction