Stich, Sebastian U.

56 publications

TMLR 2025 A Bias Correction Mechanism for Distributed Asynchronous Optimization Yuan Gao, Yuki Takezawa, Sebastian U Stich

ICLRW 2025 Breaking the Likelihood--Quality Trade-Off in Diffusion Models by Merging Pretrained Experts Yasin Esfandiari, Stefan Bauer, Sebastian U Stich, Andrea Dittadi

ICML 2025 Decoupled SGDA for Games with Intermittent Strategy Communication Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U Stich

ICML 2025 Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov, Sebastian U Stich

ICLRW 2025 LoRAM: Low-Rank Adaptation of Large Language Models on Manifold Xiaowen Jiang, Xun Wang, Sebastian U Stich

ICLR 2025 Optimizing $(l_0, L_1)$-Smooth Functions by Gradient Methods Daniil Vankov, Anton Rodomanov, Angelia Nedich, Lalitha Sankar, Sebastian U Stich

TMLR 2025 Personalized Federated Learning via Low-Rank Matrix Optimization Ali Dadras, Sebastian U Stich, Alp Yurtsever

NeurIPS 2025 Revisiting Consensus Error: A Fine-Grained Analysis of Local SGD Under Second-Order Data Heterogeneity Kumar Kshitij Patel, Ali Zindari, Sebastian U Stich, Lingxiao Wang

AISTATS 2025 Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis Ruichen Luo, Sebastian U Stich, Samuel Horváth, Martin Takáč

ICLR 2025 Scalable Decentralized Learning with Teleportation Yuki Takezawa, Sebastian U Stich

ICLR 2025 Towards Faster Decentralized Stochastic Optimization with Communication Compression Rustem Islamov, Yuan Gao, Sebastian U Stich

ICLR 2024 An Improved Analysis of Per-Sample and Per-Update Clipping in Federated Learning Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U Stich

ICLR 2024 Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates Siqi Zhang, Sayantan Choudhury, Sebastian U Stich, Nicolas Loizou

NeurIPSW 2024 DADA: Dual Averaging with Distance Adaptation Mohammad Moshtaghifar, Anton Rodomanov, Daniil Vankov, Sebastian U Stich

ICMLW 2024 Decoupled Stochastic Gradient Descent for N-Player Games Ali Zindari, Parham Yazdkhasti, Tatjana Chavdarova, Sebastian U Stich

ICLR 2024 EControl: Fast Distributed Optimization with Compression and Error Control Yuan Gao, Rustem Islamov, Sebastian U Stich

ICML 2024 Federated Optimization with Doubly Regularized Drift Correction Xiaowen Jiang, Anton Rodomanov, Sebastian U Stich

TMLR 2024 Locally Adaptive Federated Learning Sohom Mukherjee, Nicolas Loizou, Sebastian U Stich

ICML 2024 Non-Convex Stochastic Composite Optimization with Polyak Momentum Yuan Gao, Anton Rodomanov, Sebastian U Stich

ICML 2024 On Convergence of Incremental Gradient for Non-Convex Smooth Functions Anastasia Koloskova, Nikita Doikov, Sebastian U Stich, Martin Jaggi

NeurIPSW 2024 Personalized Federated Learning via Low-Rank Matrix Factorization Ali Dadras, Sebastian U Stich, Alp Yurtsever

ICML 2024 Spectral Preconditioning for Gradient Methods on Graded Non-Convex Functions Nikita Doikov, Sebastian U Stich, Martin Jaggi

NeurIPS 2024 Stabilized Proximal-Point Methods for Federated Optimization Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich

TMLR 2024 Synthetic Data Shuffling Accelerates the Convergence of Federated Learning Under Data Heterogeneity Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U Stich

COLT 2024 The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro

NeurIPS 2023 Adaptive SGD with Polyak Stepsize and Line-Search: Robust Convergence and Variance Reduction Xiaowen Jiang, Sebastian U Stich

NeurIPSW 2023 Diversity-Adjusted Adaptive Step Size Parham Yazdkhasti, Xiaowen Jiang, Sebastian U Stich

NeurIPSW 2023 Noise Injection Irons Out Local Minima and Saddle Points Konstantin Mishchenko, Sebastian U Stich

NeurIPSW 2023 On the Convergence of Local SGD Under Third-Order Smoothness and Hessian Similarity Ali Zindari, Ruichen Luo, Sebastian U Stich

CVPR 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

ICML 2023 Revisiting Gradient Clipping: Stochastic Bias and Tight Convergence Guarantees Anastasia Koloskova, Hadrien Hendrikx, Sebastian U Stich

ICML 2023 Special Properties of Gradient Descent with Large Learning Rates Amirkeivan Mohtashami, Martin Jaggi, Sebastian U Stich

NeurIPSW 2022 Bidirectional Adaptive Communication for Heterogeneous Distributed Learning Dmitrii Avdiukhin, Vladimir Braverman, Nikita Ivkin, Sebastian U Stich

NeurIPSW 2022 Data-Heterogeneity-Aware Mixing for Decentralized Learning Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U Stich

NeurIPS 2022 Decentralized Local Stochastic Extra-Gradient for Variational Inequalities Aleksandr Beznosikov, Pavel Dvurechenskii, Anastasiia Koloskova, Valentin Samokhin, Sebastian U Stich, Alexander Gasnikov

NeurIPSW 2022 Preserving Privacy with PATE for Heterogeneous Data Akshay Dodwadmath, Sebastian U Stich

NeurIPS 2022 Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasiia Koloskova, Sebastian U Stich, Martin Jaggi

FnTML 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

NeurIPS 2021 An Improved Analysis of Gradient Tracking for Decentralized Machine Learning Anastasiia Koloskova, Tao Lin, Sebastian U Stich

NeurIPS 2021 Breaking the Centralized Barrier for Cross-Device Federated Learning Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U Stich, Ananda Theertha Suresh

NeurIPS 2021 RelaySum for Decentralized Deep Learning on Heterogeneous Data Thijs Vogels, Lie He, Anastasiia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U Stich, Martin Jaggi

ICCV 2021 Semantic Perturbations with Normalizing Flows for Improved Generalization Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova

ICMLW 2021 Semantic Perturbations with Normalizing Flows for Improved Generalization Oğuz Kaan Yüksel, Sebastian U Stich, Martin Jaggi, Tatjana Chavdarova

ICLR 2021 Taming GANs with Lookahead-Minmax Tatjana Chavdarova, Matteo Pagliardini, Sebastian U Stich, François Fleuret, Martin Jaggi

ICLR 2020 Decentralized Deep Learning with Arbitrary Communication Compression Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi

ICLR 2020 Don't Use Large Mini-Batches, Use Local SGD Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi

ICLR 2020 Dynamic Model Pruning with Feedback Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi

NeurIPS 2020 Ensemble Distillation for Robust Model Fusion in Federated Learning Tao Lin, Lingjing Kong, Sebastian U Stich, Martin Jaggi

JMLR 2020 The Error-Feedback Framework: SGD with Delayed Gradients Sebastian U. Stich, Sai Praneeth Karimireddy

AISTATS 2019 Efficient Greedy Coordinate Descent for Composite Problems Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi

ICLR 2019 Local SGD Converges Fast and Communicates Little Sebastian U. Stich

NeurIPS 2018 Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization Robert Gower, Filip Hanzely, Peter Richtarik, Sebastian U Stich

AISTATS 2018 Adaptive Balancing of Gradient and Update Computation Times Using Global Geometry and Approximate Subproblems Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi

NeurIPS 2018 Sparsified SGD with Memory Sebastian U Stich, Jean-Baptiste Cordonnier, Martin Jaggi

ICML 2017 Approximate Steepest Coordinate Descent Sebastian U. Stich, Anant Raj, Martin Jaggi

NeurIPS 2017 Safe Adaptive Importance Sampling Sebastian U Stich, Anant Raj, Martin Jaggi