Sebastian U. Stich

According to our database, Sebastian U. Stich authored at least 78 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Non-Convex Stochastic Composite Optimization with Polyak Momentum.

Yuan Gao, Anton Rodomanov, Sebastian U. Stich

CoRR, 2024

2023

Stochastic distributed learning with gradient quantization and double-variance reduction.

Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich

Optim. Methods Softw., January, 2023

EControl: Fast Distributed Optimization with Compression and Error Control.

Yuan Gao, Rustem Islamov, Sebastian U. Stich

CoRR, 2023

Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes.

Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich

CoRR, 2023

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity.

Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

CoRR, 2023

Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates.

Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou

CoRR, 2023

Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders.

Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi

CoRR, 2023

Decentralized Gradient Tracking with Local Steps.

Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich

CoRR, 2023

Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction.

Xiaowen Jiang, Sebastian U. Stich

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Special Properties of Gradient Descent with Large Learning Rates.

Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich

Proceedings of the International Conference on Machine Learning, 2023

Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees.

Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich

Proceedings of the International Conference on Machine Learning, 2023

On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

CoRR, 2022

On Avoiding Local Minima Using Gradient Descent With Large Learning Rates.

Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich

CoRR, 2022

Data-heterogeneity-aware Mixing for Decentralized Learning.

Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich

CoRR, 2022

Tackling benign nonconvexity with smoothing and stochastic gradients.

Harsh Vardhan, Sebastian U. Stich

CoRR, 2022

Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods.

Amirkeivan Mohtashami, Sebastian U. Stich, Martin Jaggi

CoRR, 2022

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning.

Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities.

Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz

Proceedings of the International Conference on Machine Learning, 2022

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

Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik

Proceedings of the International Conference on Machine Learning, 2022

Masked Training of Neural Networks with Partial Gradients.

Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Advances and Open Problems in Federated Learning.

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

Found. Trends Mach. Learn., 2021

The Peril of Popular Deep Learning Uncertainty Estimation Methods.

Yehao Liu, Matteo Pagliardini, Tatjana Chavdarova, Sebastian U. Stich

CoRR, 2021

Linear Speedup in Personalized Collaborative Learning.

El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi

CoRR, 2021

On Second-order Optimization Methods for Federated Learning.

Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich

CoRR, 2021

A Field Guide to Federated Optimization.

Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

CoRR, 2021

Simultaneous Training of Partially Masked Neural Networks.

Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich

CoRR, 2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning.

Anastasia Koloskova, Tao Lin, Sebastian U. Stich

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Breaking the centralized barrier for cross-device federated learning.

Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data.

Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

Proceedings of the 38th International Conference on Machine Learning, 2021

Consensus Control for Decentralized Deep Learning.

Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich

Proceedings of the 38th International Conference on Machine Learning, 2021

Taming GANs with Lookahead-Minmax.

Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich, François Fleuret, Martin Jaggi

Proceedings of the 9th International Conference on Learning Representations, 2021

Semantic Perturbations with Normalizing Flows for Improved Generalization.

Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates.

Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads.

Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On Communication Compression for Distributed Optimization on Heterogeneous Data.

Sebastian U. Stich

CoRR, 2020

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning.

Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

CoRR, 2020

Analysis of SGD with Biased Gradient Estimators.

Ahmad Ajalloeian, Sebastian U. Stich

CoRR, 2020

Ensemble Distillation for Robust Model Fusion in Federated Learning.

Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Is Local SGD Better than Minibatch SGD?

Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

Extrapolation for Large-batch Training in Deep Learning.

Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

Proceedings of the 37th International Conference on Machine Learning, 2020

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates.

Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich

Proceedings of the 37th International Conference on Machine Learning, 2020

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning.

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

Proceedings of the 37th International Conference on Machine Learning, 2020

Don't Use Large Mini-batches, Use Local SGD.

Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

Dynamic Model Pruning with Feedback.

Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

Decentralized Deep Learning with Arbitrary Communication Compression.

Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Advances and Open Problems in Federated Learning.

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

CoRR, 2019

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning.

Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh

CoRR, 2019

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication.

Sebastian U. Stich, Sai Praneeth Karimireddy

CoRR, 2019

Unified Optimal Analysis of the (Stochastic) Gradient Method.

Sebastian U. Stich

CoRR, 2019

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication.

Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the 36th International Conference on Machine Learning, 2019

Error Feedback Fixes SignSGD and other Gradient Compression Schemes.

Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi

Proceedings of the 36th International Conference on Machine Learning, 2019

Local SGD Converges Fast and Communicates Little.

Sebastian U. Stich

Proceedings of the 7th International Conference on Learning Representations, 2019

Efficient Greedy Coordinate Descent for Composite Problems.

Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Don't Use Large Mini-Batches, Use Local SGD.

Tao Lin, Sebastian U. Stich, Martin Jaggi

CoRR, 2018

Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients.

Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

CoRR, 2018

SVRG meets SAGA: k-SVRG - A Tale of Limited Memory.

Anant Raj, Sebastian U. Stich

CoRR, 2018

Revisiting First-Order Convex Optimization Over Linear Spaces.

Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi

CoRR, 2018

Sparsified SGD with Memory.

Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization.

Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Matching Pursuit and Coordinate Descent.

Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi

Proceedings of the 35th International Conference on Machine Learning, 2018

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems.

Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Efficiency of the Accelerated Coordinate Descent Method on Structured Optimization Problems.

Yurii E. Nesterov, Sebastian U. Stich

SIAM J. Optim., 2017

On the existence of ordinary triangles.

Radoslav Fulek, Hossein Nassajian Mojarrad, Márton Naszódi, József Solymosi, Sebastian U. Stich, May Szedlák

Comput. Geom., 2017

Safe Adaptive Importance Sampling.

Sebastian U. Stich, Anant Raj, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Approximate Steepest Coordinate Descent.

Sebastian U. Stich, Anant Raj, Martin Jaggi

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

On Two Continuum Armed Bandit Problems in High Dimensions.

Hemant Tyagi, Sebastian U. Stich, Bernd Gärtner

Theory Comput. Syst., 2016

Variable metric random pursuit.

Sebastian U. Stich, Christian L. Müller, Bernd Gärtner

Math. Program., 2016

2014

Convex Optimization with Random Pursuit.

Sebastian U. Stich

PhD thesis, 2014

On low complexity Acceleration Techniques for Randomized Optimization: Supplementary Online Material.

Sebastian U. Stich

CoRR, 2014

On Low Complexity Acceleration Techniques for Randomized Optimization.

Sebastian Urban Stich

Proceedings of the Parallel Problem Solving from Nature - PPSN XIII, 2014

2013

Optimization of Convex Functions with Random Pursuit.

Sebastian U. Stich, Christian L. Müller, Bernd Gärtner

SIAM J. Optim., 2013

Stochastic continuum armed bandit problem of few linear parameters in high dimensions.

Hemant Tyagi, Sebastian U. Stich, Bernd Gärtner

CoRR, 2013

2012

On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes.

Sebastian U. Stich, Christian L. Müller

Proceedings of the Parallel Problem Solving from Nature - PPSN XII, 2012

2009

On Two Problems Regarding the Hamiltonian Cycle Game.

Dan Hefetz, Sebastian U. Stich

Electron. J. Comb., 2009