On Two Problems Regarding the Hamiltonian Cycle Game. - Dan Hefetz; Sebastian U. Stich (Electron. J. Comb., 2009)

On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes. - Sebastian U. Stich; Christian L. Müller (PPSN (1), 2012)

Stochastic continuum armed bandit problem of few linear parameters in high dimensions. - Hemant Tyagi; Sebastian U. Stich; Bernd Gärtner (CoRR, 2013)

Optimization of Convex Functions with Random Pursuit. - Sebastian U. Stich; Christian L. Müller; Bernd Gärtner (SIAM J. Optim., 2013)

On Low Complexity Acceleration Techniques for Randomized Optimization. - Sebastian Urban Stich (PPSN, 2014)

On low complexity Acceleration Techniques for Randomized Optimization: Supplementary Online Material. - Sebastian U. Stich (CoRR, 2014)

Convex Optimization with Random Pursuit. - Sebastian U. Stich (2014)

Variable metric random pursuit. - Sebastian U. Stich; Christian L. Müller; Bernd Gärtner (Math. Program., 2016)

On Two Continuum Armed Bandit Problems in High Dimensions. - Hemant Tyagi; Sebastian U. Stich; Bernd Gärtner (Theory Comput. Syst., 2016)

Approximate Steepest Coordinate Descent. - Sebastian U. Stich; Anant Raj; Martin Jaggi (ICML, 2017)

Safe Adaptive Importance Sampling. - Sebastian U. Stich; Anant Raj; Martin Jaggi (NIPS, 2017)

On the existence of ordinary triangles. - Radoslav Fulek; Hossein Nassajian Mojarrad; Márton Naszódi; József Solymosi; Sebastian U. Stich; May Szedlák (Comput. Geom., 2017)

Efficiency of the Accelerated Coordinate Descent Method on Structured Optimization Problems. - Yurii E. Nesterov; Sebastian U. Stich (SIAM J. Optim., 2017)

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. - Sai Praneeth Reddy Karimireddy; Sebastian U. Stich; Martin Jaggi (AISTATS, 2018)

On Matching Pursuit and Coordinate Descent. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (ICML, 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 (NeurIPS, 2018)

Sparsified SGD with Memory. - Sebastian U. Stich; Jean-Baptiste Cordonnier; Martin Jaggi (NeurIPS, 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)

SVRG meets SAGA: k-SVRG - A Tale of Limited Memory. - Anant Raj; Sebastian U. Stich (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)

Don't Use Large Mini-Batches, Use Local SGD. - Tao Lin 004; Sebastian U. Stich; Martin Jaggi (CoRR, 2018)

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

Local SGD Converges Fast and Communicates Little. - Sebastian U. Stich (ICLR (Poster), 2019)

Error Feedback Fixes SignSGD and other Gradient Compression Schemes. - Sai Praneeth Karimireddy; Quentin Rebjock; Sebastian U. Stich; Martin Jaggi (ICML, 2019)

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. - Anastasia Koloskova; Sebastian U. Stich; Martin Jaggi (ICML, 2019)

Unified Optimal Analysis of the (Stochastic) Gradient Method. - Sebastian U. Stich (CoRR, 2019)

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. - Sebastian U. Stich; Sai Praneeth Karimireddy (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)

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; et al. (CoRR, 2019)

Decentralized Deep Learning with Arbitrary Communication Compression. - Anastasia Koloskova; Tao Lin 004; Sebastian U. Stich; Martin Jaggi (ICLR, 2020)

Dynamic Model Pruning with Feedback. - Tao Lin 004; Sebastian U. Stich; Luis Barba; Daniil Dmitriev; Martin Jaggi (ICLR, 2020)

Don't Use Large Mini-batches, Use Local SGD. - Tao Lin 004; Sebastian U. Stich; Kumar Kshitij Patel; Martin Jaggi (ICLR, 2020)

SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. - Sai Praneeth Karimireddy; Satyen Kale; Mehryar Mohri; Sashank J. Reddi; Sebastian U. Stich; Ananda Theertha Suresh (ICML, 2020)

A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. - Anastasia Koloskova; Nicolas Loizou; Sadra Boreiri; Martin Jaggi; Sebastian U. Stich (ICML, 2020)

Extrapolation for Large-batch Training in Deep Learning. - Tao Lin 004; Lingjing Kong 001; Sebastian U. Stich; Martin Jaggi (ICML, 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 (ICML, 2020)

Ensemble Distillation for Robust Model Fusion in Federated Learning. - Tao Lin 004; Lingjing Kong 001; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2020)

Analysis of SGD with Biased Gradient Estimators. - Ahmad Ajalloeian; 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)

On Communication Compression for Distributed Optimization on Heterogeneous Data. - Sebastian U. Stich (CoRR, 2020)

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. - Hossein Shokri Ghadikolaei; Sebastian U. Stich; Martin Jaggi (AISTATS, 2021)

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! - Dmitry Kovalev; Anastasia Koloskova; Martin Jaggi; Peter Richtárik; Sebastian U. Stich (AISTATS, 2021)

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. - Sebastian U. Stich; Amirkeivan Mohtashami; Martin Jaggi (AISTATS, 2021)

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

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

Consensus Control for Decentralized Deep Learning. - Lingjing Kong 001; Tao Lin 004; Anastasia Koloskova; Martin Jaggi; Sebastian U. Stich (ICML, 2021)

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. - Tao Lin 004; Sai Praneeth Karimireddy; Sebastian U. Stich; Martin Jaggi (ICML, 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 (NeurIPS, 2021)

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. - Anastasia Koloskova; Tao Lin 004; Sebastian U. Stich (NeurIPS, 2021)

RelaySum for Decentralized Deep Learning on Heterogeneous Data. - Thijs Vogels; Lie He; Anastasia Koloskova; Sai Praneeth Karimireddy; Tao Lin 004; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2021)

Simultaneous Training of Partially Masked Neural Networks. - Amirkeivan Mohtashami; Martin Jaggi; Sebastian U. Stich (CoRR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

On Second-order Optimization Methods for Federated Learning. - Sebastian Bischoff 001; Stephan Günnemann; Martin Jaggi; 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)

The Peril of Popular Deep Learning Uncertainty Estimation Methods. - Yehao Liu; Matteo Pagliardini; Tatjana Chavdarova; Sebastian U. Stich (CoRR, 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; et al. (Found. Trends Mach. Learn., 2021)

Masked Training of Neural Networks with Partial Gradients. - Amirkeivan Mohtashami; Martin Jaggi; Sebastian U. Stich (AISTATS, 2022)

ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! - Konstantin Mishchenko; Grigory Malinovsky; Sebastian U. Stich; Peter Richtárik (ICML, 2022)

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. - Hui-Po Wang; Sebastian U. Stich; Yang He 005; Mario Fritz (ICML, 2022)

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. - Aleksandr Beznosikov; Pavel E. Dvurechensky; Anastasia Koloskova; Valentin Samokhin; Sebastian U. Stich; Alexander V. Gasnikov (NeurIPS, 2022)

Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. - Anastasia Koloskova; Sebastian U. Stich; Martin Jaggi (NeurIPS, 2022)

Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods. - Amirkeivan Mohtashami; Sebastian U. Stich; Martin Jaggi (CoRR, 2022)

Tackling benign nonconvexity with smoothing and stochastic gradients. - Harsh Vardhan; Sebastian U. Stich (CoRR, 2022)

Data-heterogeneity-aware Mixing for Decentralized Learning. - Yatin Dandi; Anastasia Koloskova; Martin Jaggi; 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)

Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm; Sebastian U. Stich (CoRR, 2022)

On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm; Sebastian U. Stich (CVPR, 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 (ICML, 2023)

Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction. - Xiaowen Jiang; Sebastian U. Stich (NeurIPS, 2023)

Decentralized Gradient Tracking with Local Steps. - Yue Liu; Tao Lin 004; Anastasia Koloskova; Sebastian U. Stich (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)

Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes. - Sohom Mukherjee; Nicolas Loizou; Sebastian U. Stich (CoRR, 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., 2023)

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 (COLT, 2024)

An improved analysis of per-sample and per-update clipping in federated learning. - Bo Li 119; Xiaowen Jiang; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Sebastian U. Stich (ICLR, 2024)

EControl: Fast Distributed Optimization with Compression and Error Control. - Yuan Gao 025; Rustem Islamov; 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 (ICLR, 2024)

Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. - Nikita Doikov; Sebastian U. Stich; Martin Jaggi (ICML, 2024)

Non-convex Stochastic Composite Optimization with Polyak Momentum. - Yuan Gao 025; Anton Rodomanov; Sebastian U. Stich (ICML, 2024)

Federated Optimization with Doubly Regularized Drift Correction. - Xiaowen Jiang; 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 (ICML, 2024)

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

Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction. - Anton Rodomanov; Xiaowen Jiang; Sebastian U. Stich (NeurIPS, 2024)

Near Optimal Decentralized Optimization with Compression and Momentum Tracking. - Rustem Islamov; Yuan Gao 025; Sebastian U. Stich (CoRR, 2024)

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. - Bo Li 119; Yasin Esfandiari; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Sebastian U. Stich (Trans. Mach. Learn. Res., 2024)

Locally Adaptive Federated Learning. - Sohom Mukherjee; Nicolas Loizou; Sebastian U. Stich (Trans. Mach. Learn. Res., 2024)

Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis. - Ruichen Luo; Sebastian U. Stich; Samuel Horváth; Martin Takác 001 (AISTATS, 2025)

Towards Faster Decentralized Stochastic Optimization with Communication Compression. - Rustem Islamov; Yuan Gao 025; Sebastian U. Stich (ICLR, 2025)

Scalable Decentralized Learning with Teleportation. - Yuki Takezawa; Sebastian U. Stich (ICLR, 2025)

Optimizing (L0, L1)-Smooth Functions by Gradient Methods. - Daniil Vankov; Anton Rodomanov; Angelia Nedich; Lalitha Sankar; Sebastian U. Stich (ICLR, 2025)

Personalized Federated Learning via Low-Rank Matrix Optimization. - Ali Dadras; Sebastian U. Stich; Alp Yurtsever (Trans. Mach. Learn. Res., 2025)

DADA: Dual Averaging with Distance Adaptation. - Mohammad Moshtaghifar; Anton Rodomanov; Daniil Vankov; Sebastian U. Stich (CoRR, 2025)

Decoupled SGDA for Games with Intermittent Strategy Communication. - Ali Zindari; Parham Yazdkhasti; Anton Rodomanov; Tatjana Chavdarova; Sebastian U. Stich (CoRR, 2025)

Accelerated Distributed Optimization with Compression and Error Feedback. - Yuan Gao 025; Anton Rodomanov; Jeremy Rack; Sebastian U. Stich (CoRR, 2025)

Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization. - Yuki Takezawa; Xiaowen Jiang; Anton Rodomanov; Sebastian U. Stich (CoRR, 2025)