| 2026 | COLT | On the Stability of Nonlinear Dynamics in GD and SGD: Beyond Quadratic Potentials. | Rotem Mulayoff, Sebastian U. Stich |
| 2025 | AISTATS | Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. | Ruichen Luo, Sebastian U. Stich, Samuel Horvth, Martin Takc |
| 2025 | ICLR | Towards Faster Decentralized Stochastic Optimization with Communication Compression. | Rustem Islamov, Yuan Gao, Sebastian U. Stich |
| 2025 | ICLR | Scalable Decentralized Learning with Teleportation. | Yuki Takezawa, Sebastian U. Stich |
| 2025 | ICLR | Optimizing (L0, L1)-Smooth Functions by Gradient Methods. | Daniil Vankov, Anton Rodomanov, Angelia Nedich, Lalitha Sankar, Sebastian U. Stich |
| 2025 | ICML | Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. | Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich |
| 2025 | ICML | Decoupled SGDA for Games with Intermittent Strategy Communication. | Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U. Stich |
| 2024 | COLT | 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 |
| 2024 | ICLR | An improved analysis of per-sample and per-update clipping in federated learning. | Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrm, Sebastian U. Stich |
| 2024 | ICLR | EControl: Fast Distributed Optimization with Compression and Error Control. | Yuan Gao, Rustem Islamov, Sebastian U. Stich |
| 2024 | ICLR | Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. | Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou |
| 2024 | ICML | Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. | Nikita Doikov, Sebastian U. Stich, Martin Jaggi |
| 2024 | ICML | Non-convex Stochastic Composite Optimization with Polyak Momentum. | Yuan Gao, Anton Rodomanov, Sebastian U. Stich |
| 2024 | ICML | Federated Optimization with Doubly Regularized Drift Correction. | Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich |
| 2024 | ICML | On Convergence of Incremental Gradient for Non-convex Smooth Functions. | Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi |
| 2023 | CVPR | On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. | Bo Li, Mikkel N. Schmidt, Tommy S. Alstrm, Sebastian U. Stich |
| 2023 | ICML | Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. | Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich |
| 2023 | ICML | Special Properties of Gradient Descent with Large Learning Rates. | Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich |
| 2022 | AISTATS | Masked Training of Neural Networks with Partial Gradients. | Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich |
| 2022 | ICML | ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! | Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtrik |
| 2022 | ICML | ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. | Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz |
| 2021 | AISTATS | LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. | Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi |
| 2021 | AISTATS | A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! | Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtrik, Sebastian U. Stich |
| 2021 | AISTATS | Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. | Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi |
| 2021 | ICCV | Semantic Perturbations with Normalizing Flows for Improved Generalization. | Oguz Kaan Yksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova |
| 2021 | ICLR | Taming GANs with Lookahead-Minmax. | Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich, Franois Fleuret, Martin Jaggi |
| 2021 | ICML | Consensus Control for Decentralized Deep Learning. | Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich |
| 2021 | ICML | Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. | Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi |
| 2020 | ICLR | Decentralized Deep Learning with Arbitrary Communication Compression. | Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi |
| 2020 | ICLR | Dynamic Model Pruning with Feedback. | Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi |
| 2020 | ICLR | Don't Use Large Mini-batches, Use Local SGD. | Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi |
| 2020 | ICML | SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. | Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh |
| 2020 | ICML | A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. | Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich |
| 2020 | ICML | Extrapolation for Large-batch Training in Deep Learning. | Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi |
| 2020 | ICML | 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 |
| 2019 | AISTATS | Efficient Greedy Coordinate Descent for Composite Problems. | Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi |
| 2019 | ICLR | Local SGD Converges Fast and Communicates Little. | Sebastian U. Stich |
| 2019 | ICML | Error Feedback Fixes SignSGD and other Gradient Compression Schemes. | Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi |
| 2019 | ICML | Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. | Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi |
| 2018 | AISTATS | Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. | Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi |
| 2018 | ICML | On Matching Pursuit and Coordinate Descent. | Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rtsch, Bernhard Schlkopf, Sebastian U. Stich, Martin Jaggi |
| 2017 | ICML | Approximate Steepest Coordinate Descent. | Sebastian U. Stich, Anant Raj, Martin Jaggi |
| 2012 | PPSN | On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes. | Sebastian U. Stich, Christian L. Mller |