Sebastian U. Stich

144 papers A* 44A 7Journal 89Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Enhancing LLM Training via Spectral Clipping. CoRR Xiaowen Jiang, Andrei Semenov, Sebastian U. Stich
2026	J	`jnl`	On the Stability of Nonlinear Dynamics in GD and SGD: Beyond Quadratic Potentials. CoRR Rotem Mulayoff, Sebastian U. Stich
2026	J	`jnl`	Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning. CoRR Polina Dolgova, Sebastian U. Stich
2025	J	`jnl`	A Bias Correction Mechanism for Distributed Asynchronous Optimization. Trans. Mach. Learn. Res. Yuan Gao, Yuki Takezawa, Sebastian U. Stich
2025	J	`jnl`	Accelerated Distributed Optimization with Compression and Error Feedback. CoRR Yuan Gao, Anton Rodomanov, Jeremy Rack, Sebastian U. Stich
2025	J	`jnl`	Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts. CoRR Yasin Esfandiari, Stefan Bauer, Sebastian U. Stich, Andrea Dittadi
2025	J	`jnl`	Composite Optimization with Error Feedback: the Dual Averaging Approach. CoRR Yuan Gao, Anton Rodomanov, Jeremy Rack, Sebastian U. Stich
2025	J	`jnl`	DADA: Dual Averaging with Distance Adaptation. CoRR Mohammad Moshtaghifar, Anton Rodomanov, Daniil Vankov, Sebastian U. Stich
2025	J	`jnl`	Decentralized gradient tracking with local steps. Optim. Methods Softw. Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich
2025	A*	`conf`	Decoupled SGDA for Games with Intermittent Strategy Communication. ICML Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U. Stich
2025	J	`jnl`	Decoupled SGDA for Games with Intermittent Strategy Communication. CoRR Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U. Stich
2025	A*	`conf`	Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. ICML Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2025	J	`jnl`	Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. CoRR Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2025	J	`jnl`	FedMuon: Federated Learning with Bias-corrected LMO-based Optimization. CoRR Yuki Takezawa, Anastasia Koloskova, Xiaowen Jiang, Sebastian U. Stich
2025	J	`jnl`	Monotone Near-Zero-Sum Games: A Generalization of Convex-Concave Minimax. CoRR Ruichen Luo, Sebastian U. Stich, Krishnendu Chatterjee
2025	J	`jnl`	Non-Convex Federated Optimization under Cost-Aware Client Selection. CoRR Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2025	A*	`conf`	Optimizing (L0, L1)-Smooth Functions by Gradient Methods. ICLR Daniil Vankov, Anton Rodomanov, Angelia Nedich, Lalitha Sankar, Sebastian U. Stich
2025	J	`jnl`	Personalized Federated Learning via Low-Rank Matrix Optimization. Trans. Mach. Learn. Res. Ali Dadras, Sebastian U. Stich, Alp Yurtsever
2025	A	`conf`	Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. AISTATS Ruichen Luo, Sebastian U. Stich, Samuel Horváth, Martin Takác
2025	J	`jnl`	Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. CoRR Ruichen Luo, Sebastian U. Stich, Samuel Horváth, Martin Takác
2025	A*	`conf`	Scalable Decentralized Learning with Teleportation. ICLR Yuki Takezawa, Sebastian U. Stich
2025	J	`jnl`	Scalable Decentralized Learning with Teleportation. CoRR Yuki Takezawa, Sebastian U. Stich
2025	A*	`conf`	Towards Faster Decentralized Stochastic Optimization with Communication Compression. ICLR Rustem Islamov, Yuan Gao, Sebastian U. Stich
2024	A*	`conf`	An improved analysis of per-sample and per-update clipping in federated learning. ICLR Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich
2024	A*	`conf`	Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. ICLR Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou
2024	A*	`conf`	EControl: Fast Distributed Optimization with Compression and Error Control. ICLR Yuan Gao, Rustem Islamov, Sebastian U. Stich
2024	A*	`conf`	Federated Optimization with Doubly Regularized Drift Correction. ICML Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2024	J	`jnl`	Federated Optimization with Doubly Regularized Drift Correction. CoRR Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2024	J	`jnl`	Locally Adaptive Federated Learning. Trans. Mach. Learn. Res. Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich
2024	J	`jnl`	Near Optimal Decentralized Optimization with Compression and Momentum Tracking. CoRR Rustem Islamov, Yuan Gao, Sebastian U. Stich
2024	J	`jnl`	Non-Convex Stochastic Composite Optimization with Polyak Momentum. CoRR Yuan Gao, Anton Rodomanov, Sebastian U. Stich
2024	A*	`conf`	Non-convex Stochastic Composite Optimization with Polyak Momentum. ICML Yuan Gao, Anton Rodomanov, Sebastian U. Stich
2024	A*	`conf`	On Convergence of Incremental Gradient for Non-convex Smooth Functions. ICML Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi
2024	A*	`conf`	Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. ICML Nikita Doikov, Sebastian U. Stich, Martin Jaggi
2024	A*	`conf`	Stabilized Proximal-Point Methods for Federated Optimization. NeurIPS Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2024	J	`jnl`	Stabilized Proximal-Point Methods for Federated Optimization. CoRR Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
2024	J	`jnl`	Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. Trans. Mach. Learn. Res. Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich
2024	A*	`conf`	The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. COLT Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
2024	J	`jnl`	The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. CoRR Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
2024	A*	`conf`	Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction. NeurIPS Anton Rodomanov, Xiaowen Jiang, Sebastian U. Stich
2023	A*	`conf`	Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction. NeurIPS Xiaowen Jiang, Sebastian U. Stich
2023	J	`jnl`	Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction. CoRR Xiaowen Jiang, Sebastian U. Stich
2023	J	`jnl`	Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. CoRR Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou
2023	J	`jnl`	Decentralized Gradient Tracking with Local Steps. CoRR Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich
2023	J	`jnl`	EControl: Fast Distributed Optimization with Compression and Error Control. CoRR Yuan Gao, Rustem Islamov, Sebastian U. Stich
2023	J	`jnl`	Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes. CoRR Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich
2023	A*	`conf`	On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. CVPR Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich
2023	A*	`conf`	Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. ICML Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich
2023	J	`jnl`	Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. CoRR Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich
2023	J	`jnl`	Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders. CoRR Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi
2023	A*	`conf`	Special Properties of Gradient Descent with Large Learning Rates. ICML Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich
2023	J	`jnl`	Stochastic distributed learning with gradient quantization and double-variance reduction. Optim. Methods Softw. Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich
2023	J	`jnl`	Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. CoRR Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich
2022	J	`jnl`	An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. CoRR Anastasia Koloskova, Tao Lin, Sebastian U. Stich
2022	J	`jnl`	Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods. CoRR Amirkeivan Mohtashami, Sebastian U. Stich, Martin Jaggi
2022	J	`jnl`	Data-heterogeneity-aware Mixing for Decentralized Learning. CoRR Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
2022	A*	`conf`	Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. NeurIPS Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov
2022	A	`conf`	Masked Training of Neural Networks with Partial Gradients. AISTATS Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich
2022	J	`jnl`	On Avoiding Local Minima Using Gradient Descent With Large Learning Rates. CoRR Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich
2022	J	`jnl`	Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. CoRR Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich
2022	A*	`conf`	ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. ICML Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz
2022	A*	`conf`	ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik
2022	J	`jnl`	ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik
2022	A*	`conf`	Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. NeurIPS Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2022	J	`jnl`	Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. CoRR Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2022	J	`jnl`	Tackling benign nonconvexity with smoothing and stochastic gradients. CoRR Harsh Vardhan, Sebastian U. Stich
2021	J	`jnl`	A Field Guide to Federated Optimization. CoRR 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
2021	A	`conf`	A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	A*	`conf`	An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. NeurIPS Anastasia Koloskova, Tao Lin, Sebastian U. Stich
2021	A*	`conf`	Breaking the centralized barrier for cross-device federated learning. NeurIPS Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2021	A*	`conf`	Consensus Control for Decentralized Deep Learning. ICML Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
2021	J	`jnl`	Consensus Control for Decentralized Deep Learning. CoRR Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
2021	A	`conf`	Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. AISTATS Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi
2021	J	`jnl`	Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. CoRR Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi
2021	J	`jnl`	Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. CoRR Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov
2021	A	`conf`	LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. AISTATS Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi
2021	J	`jnl`	Linear Speedup in Personalized Collaborative Learning. CoRR El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi
2021	J	`jnl`	On Second-order Optimization Methods for Federated Learning. CoRR Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich
2021	J	`jnl`	ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. CoRR Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz
2021	J	`jnl`	Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2021	A*	`conf`	Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2021	A*	`conf`	RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi
2021	J	`jnl`	RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2021	A*	`conf`	Semantic Perturbations with Normalizing Flows for Improved Generalization. ICCV Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova
2021	J	`jnl`	Semantic Perturbations with Normalizing Flows for Improved Generalization. CoRR Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova
2021	J	`jnl`	Simultaneous Training of Partially Masked Neural Networks. CoRR Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich
2021	A*	`conf`	Taming GANs with Lookahead-Minmax. ICLR Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich, François Fleuret, Martin Jaggi
2021	J	`jnl`	The Peril of Popular Deep Learning Uncertainty Estimation Methods. CoRR Yehao Liu, Matteo Pagliardini, Tatjana Chavdarova, Sebastian U. Stich
2020	J	`jnl`	A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich
2020	A*	`conf`	A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. ICML Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
2020	J	`jnl`	A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich
2020	J	`jnl`	Analysis of SGD with Biased Gradient Estimators. CoRR Ahmad Ajalloeian, Sebastian U. Stich
2020	A*	`conf`	Decentralized Deep Learning with Arbitrary Communication Compression. ICLR Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi
2020	A*	`conf`	Don't Use Large Mini-batches, Use Local SGD. ICLR Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi
2020	A*	`conf`	Dynamic Model Pruning with Feedback. ICLR Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi
2020	J	`jnl`	Dynamic Model Pruning with Feedback. CoRR Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi
2020	A*	`conf`	Ensemble Distillation for Robust Model Fusion in Federated Learning. NeurIPS Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
2020	J	`jnl`	Ensemble Distillation for Robust Model Fusion in Federated Learning. CoRR Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
2020	A*	`conf`	Extrapolation for Large-batch Training in Deep Learning. ICML Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
2020	J	`jnl`	Extrapolation for Large-batch Training in Deep Learning. CoRR Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
2020	A*	`conf`	Is Local SGD Better than Minibatch SGD? ICML Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	J	`jnl`	Is Local SGD Better than Minibatch SGD? CoRR Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	J	`jnl`	Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2020	J	`jnl`	On Communication Compression for Distributed Optimization on Heterogeneous Data. CoRR Sebastian U. Stich
2020	A*	`conf`	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	J	`jnl`	Decentralized Deep Learning with Arbitrary Communication Compression. CoRR Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi
2019	A*	`conf`	Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. ICML Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2019	J	`jnl`	Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. CoRR Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2019	A	`conf`	Efficient Greedy Coordinate Descent for Composite Problems. AISTATS Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2019	A*	`conf`	Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi
2019	J	`jnl`	Error Feedback Fixes SignSGD and other Gradient Compression Schemes. CoRR Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi
2019	—	`conf`	Local SGD Converges Fast and Communicates Little. ICLR (Poster) Sebastian U. Stich
2019	J	`jnl`	SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh
2019	J	`jnl`	The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. CoRR Sebastian U. Stich, Sai Praneeth Karimireddy
2019	J	`jnl`	Unified Optimal Analysis of the (Stochastic) Gradient Method. CoRR Sebastian U. Stich
2018	A*	`conf`	Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. NeurIPS Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
2018	J	`jnl`	Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. CoRR Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich
2018	A	`conf`	Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. AISTATS Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Don't Use Large Mini-Batches, Use Local SGD. CoRR Tao Lin, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Efficient Greedy Coordinate Descent for Composite Problems. CoRR Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. CoRR Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Local SGD Converges Fast and Communicates Little. CoRR Sebastian U. Stich
2018	A*	`conf`	On Matching Pursuit and Coordinate Descent. ICML Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
2018	J	`jnl`	SVRG meets SAGA: k-SVRG - A Tale of Limited Memory. CoRR Anant Raj, Sebastian U. Stich
2018	A*	`conf`	Sparsified SGD with Memory. NeurIPS Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
2018	J	`jnl`	Sparsified SGD with Memory. CoRR Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi
2017	A*	`conf`	Approximate Steepest Coordinate Descent. ICML Sebastian U. Stich, Anant Raj, Martin Jaggi
2017	J	`jnl`	Approximate Steepest Coordinate Descent. CoRR Sebastian U. Stich, Anant Raj, Martin Jaggi
2017	J	`jnl`	Efficiency of the Accelerated Coordinate Descent Method on Structured Optimization Problems. SIAM J. Optim. Yurii E. Nesterov, Sebastian U. Stich
2017	J	`jnl`	On the existence of ordinary triangles. Comput. Geom. Radoslav Fulek, Hossein Nassajian Mojarrad, Márton Naszódi, József Solymosi, Sebastian U. Stich, May Szedlák
2017	—	`conf`	Safe Adaptive Importance Sampling. NIPS Sebastian U. Stich, Anant Raj, Martin Jaggi
2017	J	`jnl`	Safe Adaptive Importance Sampling. CoRR Sebastian U. Stich, Anant Raj, Martin Jaggi
2016	J	`jnl`	On Two Continuum Armed Bandit Problems in High Dimensions. Theory Comput. Syst. Hemant Tyagi, Sebastian U. Stich, Bernd Gärtner
2016	J	`jnl`	Variable metric random pursuit. Math. Program. Sebastian U. Stich, Christian L. Müller, Bernd Gärtner
2014	—	`—`	Convex Optimization with Random Pursuit. Sebastian U. Stich
2014	J	`jnl`	On low complexity Acceleration Techniques for Randomized Optimization: Supplementary Online Material. CoRR Sebastian U. Stich
2013	J	`jnl`	Optimization of Convex Functions with Random Pursuit. SIAM J. Optim. Sebastian U. Stich, Christian L. Müller, Bernd Gärtner
2013	J	`jnl`	Stochastic continuum armed bandit problem of few linear parameters in high dimensions. CoRR Hemant Tyagi, Sebastian U. Stich, Bernd Gärtner
2012	—	`conf`	On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes. PPSN (1) Sebastian U. Stich, Christian L. Müller
2011	J	`jnl`	Optimization of Convex Functions with Random Pursuit CoRR Sebastian U. Stich, Christian L. Müller, Bernd Gärtner
2009	J	`jnl`	On Two Problems Regarding the Hamiltonian Cycle Game. Electron. J. Comb. Dan Hefetz, Sebastian U. Stich