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ORCIDSebastian U. Stich
Sebastian Urban Stich
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2020 – today
- 2024
[j9]Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich:
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. Trans. Mach. Learn. Res. 2024 (2024)- [c47]Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro:
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. COLT 2024: 4115-4157 - [c46]Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich:
An improved analysis of per-sample and per-update clipping in federated learning. ICLR 2024 - [c45]Yuan Gao, Rustem Islamov, Sebastian U. Stich:
EControl: Fast Distributed Optimization with Compression and Error Control. ICLR 2024 - [c44]Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou:
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. ICLR 2024 - [c43]Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions. ICML 2024 - [c42]Yuan Gao, Anton Rodomanov, Sebastian U. Stich:
Non-convex Stochastic Composite Optimization with Polyak Momentum. ICML 2024 - [c41]Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich:
Federated Optimization with Doubly Regularized Drift Correction. ICML 2024 - [c40]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
On Convergence of Incremental Gradient for Non-convex Smooth Functions. ICML 2024 - [i62]Yuan Gao, Anton Rodomanov, Sebastian U. Stich:
Non-Convex Stochastic Composite Optimization with Polyak Momentum. CoRR abs/2403.02967 (2024) - [i61]Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich:
Federated Optimization with Doubly Regularized Drift Correction. CoRR abs/2404.08447 (2024) - [i60]Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro:
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. CoRR abs/2405.11667 (2024) - [i59]Rustem Islamov, Yuan Gao, Sebastian U. Stich:
Near Optimal Decentralized Optimization with Compression and Momentum Tracking. CoRR abs/2405.20114 (2024) - [i58]Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich:
Stabilized Proximal-Point Methods for Federated Optimization. CoRR abs/2407.07084 (2024) - 2023
- [j8]Samuel Horváth
, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik, Sebastian U. Stich:
Stochastic distributed learning with gradient quantization and double-variance reduction. Optim. Methods Softw. 38(1): 91-106 (2023) - [c39]Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich:
On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. CVPR 2023: 3964-3973 - [c38]Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich:
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. ICML 2023: 17343-17363 - [c37]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Special Properties of Gradient Descent with Large Learning Rates. ICML 2023: 25082-25104 - [c36]Xiaowen Jiang, Sebastian U. Stich:
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction. NeurIPS 2023 - [i57]Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich:
Decentralized Gradient Tracking with Local Steps. CoRR abs/2301.01313 (2023) - [i56]Anastasia Koloskova, Hadrien Hendrikx, Sebastian U. Stich:
Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees. CoRR abs/2305.01588 (2023) - [i55]Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich, Martin Jaggi:
Shuffle SGD is Always Better than SGD: Improved Analysis of SGD with Arbitrary Data Orders. CoRR abs/2305.19259 (2023) - [i54]Siqi Zhang, Sayantan Choudhury, Sebastian U. Stich, Nicolas Loizou:
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. CoRR abs/2306.05100 (2023) - [i53]Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich:
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. CoRR abs/2306.13263 (2023) - [i52]Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich:
Locally Adaptive Federated Learning via Stochastic Polyak Stepsizes. CoRR abs/2307.06306 (2023) - [i51]Xiaowen Jiang, Sebastian U. Stich:
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction. CoRR abs/2308.06058 (2023) - [i50]Yuan Gao, Rustem Islamov, Sebastian U. Stich:
EControl: Fast Distributed Optimization with Compression and Error Control. CoRR abs/2311.05645 (2023) - 2022
- [c35]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Masked Training of Neural Networks with Partial Gradients. AISTATS 2022: 5876-5890 - [c34]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! ICML 2022: 15750-15769 - [c33]Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz:
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. ICML 2022: 23034-23054 - [c32]Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov:
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. NeurIPS 2022 - [c31]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. NeurIPS 2022 - [i49]Amirkeivan Mohtashami, Sebastian U. Stich, Martin Jaggi:
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods. CoRR abs/2202.01838 (2022) - [i48]Anastasia Koloskova, Tao Lin, Sebastian U. Stich:
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. CoRR abs/2202.03836 (2022) - [i47]Harsh Vardhan, Sebastian U. Stich:
Tackling benign nonconvexity with smoothing and stochastic gradients. CoRR abs/2202.09052 (2022) - [i46]Konstantin Mishchenko, Grigory Malinovsky, Sebastian U. Stich, Peter Richtárik:
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally! CoRR abs/2202.09357 (2022) - [i45]Yatin Dandi, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Data-heterogeneity-aware Mixing for Decentralized Learning. CoRR abs/2204.06477 (2022) - [i44]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
On Avoiding Local Minima Using Gradient Descent With Large Learning Rates. CoRR abs/2205.15142 (2022) - [i43]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. CoRR abs/2206.08307 (2022) - [i42]Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich:
Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. CoRR abs/2212.02191 (2022) - 2021
- [j7]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:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021) - [c30]Hossein Shokri Ghadikolaei, Sebastian U. Stich, Martin Jaggi:
LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. AISTATS 2021: 3943-3951 - [c29]Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. AISTATS 2021: 4042-4050 - [c28]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! AISTATS 2021: 4087-4095 - [c27]Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova:
Semantic Perturbations with Normalizing Flows for Improved Generalization. ICCV 2021: 6599-6609 - [c26]Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich, François Fleuret, Martin Jaggi:
Taming GANs with Lookahead-Minmax. ICLR 2021 - [c25]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. ICML 2021: 5686-5696 - [c24]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML 2021: 6654-6665 - [c23]Anastasia Koloskova, Tao Lin, Sebastian U. Stich:
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. NeurIPS 2021: 11422-11435 - [c22]Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS 2021: 28004-28015 - [c21]Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
Breaking the centralized barrier for cross-device federated learning. NeurIPS 2021: 28663-28676 - [i41]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2102.04761 (2021) - [i40]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. CoRR abs/2102.04828 (2021) - [i39]Sebastian U. Stich, Amirkeivan Mohtashami, Martin Jaggi:
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates. CoRR abs/2103.02351 (2021) - [i38]Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov:
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. CoRR abs/2106.08315 (2021) - [i37]Amirkeivan Mohtashami, Martin Jaggi, Sebastian U. Stich:
Simultaneous Training of Partially Masked Neural Networks. CoRR abs/2106.08895 (2021) - [i36]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:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i35]Oguz Kaan Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova:
Semantic Perturbations with Normalizing Flows for Improved Generalization. CoRR abs/2108.07958 (2021) - [i34]Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich:
On Second-order Optimization Methods for Federated Learning. CoRR abs/2109.02388 (2021) - [i33]Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2110.04175 (2021) - [i32]Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz:
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. CoRR abs/2110.05323 (2021) - [i31]El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi:
Linear Speedup in Personalized Collaborative Learning. CoRR abs/2111.05968 (2021) - [i30]Yehao Liu, Matteo Pagliardini, Tatjana Chavdarova, Sebastian U. Stich:
The Peril of Popular Deep Learning Uncertainty Estimation Methods. CoRR abs/2112.05000 (2021) - 2020
- [c20]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR 2020 - [c19]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. ICLR 2020 - [c18]Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi:
Don't Use Large Mini-batches, Use Local SGD. ICLR 2020 - [c17]Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. ICML 2020: 5132-5143 - [c16]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. ICML 2020: 5381-5393 - [c15]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. ICML 2020: 6094-6104 - [c14]Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? ICML 2020: 10334-10343 - [c13]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. NeurIPS 2020 - [i29]Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro:
Is Local SGD Better than Minibatch SGD? CoRR abs/2002.07839 (2020) - [i28]Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, Sebastian U. Stich:
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. CoRR abs/2003.10422 (2020) - [i27]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. CoRR abs/2006.05720 (2020) - [i26]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. CoRR abs/2006.07242 (2020) - [i25]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. CoRR abs/2006.07253 (2020) - [i24]Ahmad Ajalloeian, Sebastian U. Stich:
Analysis of SGD with Biased Gradient Estimators. CoRR abs/2008.00051 (2020) - [i23]Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning. CoRR abs/2008.03606 (2020) - [i22]Sebastian U. Stich:
On Communication Compression for Distributed Optimization on Heterogeneous Data. CoRR abs/2009.02388 (2020) - [i21]Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, Sebastian U. Stich:
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! CoRR abs/2011.01697 (2020)
2010 – 2019
- 2019
- [c12]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. AISTATS 2019: 2887-2896 - [c11]Sebastian U. Stich:
Local SGD Converges Fast and Communicates Little. ICLR (Poster) 2019 - [c10]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. ICML 2019: 3252-3261 - [c9]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. ICML 2019: 3478-3487 - [i20]Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. CoRR abs/1901.09847 (2019) - [i19]Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. CoRR abs/1902.00340 (2019) - [i18]Sebastian U. Stich:
Unified Optimal Analysis of the (Stochastic) Gradient Method. CoRR abs/1907.04232 (2019) - [i17]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. CoRR abs/1907.09356 (2019) - [i16]Sebastian U. Stich, Sai Praneeth Karimireddy:
The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication. CoRR abs/1909.05350 (2019) - [i15]Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh:
SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning. CoRR abs/1910.06378 (2019) - [i14]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:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [c8]Sai Praneeth Reddy Karimireddy, Sebastian U. Stich, Martin Jaggi:
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. AISTATS 2018: 1204-1213 - [c7]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
On Matching Pursuit and Coordinate Descent. ICML 2018: 3204-3213 - [c6]Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich:
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. NeurIPS 2018: 1626-1636 - [c5]Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi:
Sparsified SGD with Memory. NeurIPS 2018: 4452-4463 - [i13]Robert M. Gower, Filip Hanzely, Peter Richtárik, Sebastian U. Stich:
Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization. CoRR abs/1802.04079 (2018) - [i12]Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi:
Revisiting First-Order Convex Optimization Over Linear Spaces. CoRR abs/1803.09539 (2018) - [i11]Anant Raj, Sebastian U. Stich:
SVRG meets SAGA: k-SVRG - A Tale of Limited Memory. CoRR abs/1805.00982 (2018) - [i10]Sebastian U. Stich:
Local SGD Converges Fast and Communicates Little. CoRR abs/1805.09767 (2018) - [i9]Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients. CoRR abs/1806.00413 (2018) - [i8]Tao Lin, Sebastian U. Stich, Martin Jaggi:
Don't Use Large Mini-Batches, Use Local SGD. CoRR abs/1808.07217 (2018) - [i7]Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi:
Sparsified SGD with Memory. CoRR abs/1809.07599 (2018) - [i6]Sai Praneeth Karimireddy, Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Efficient Greedy Coordinate Descent for Composite Problems. CoRR abs/1810.06999 (2018) - 2017
- [j6]Radoslav Fulek, Hossein Nassajian Mojarrad, Márton Naszódi, József Solymosi, Sebastian U. Stich, May Szedlák:
On the existence of ordinary triangles. Comput. Geom. 66: 28-31 (2017) - [j5]Yurii E. Nesterov, Sebastian U. Stich:
Efficiency of the Accelerated Coordinate Descent Method on Structured Optimization Problems. SIAM J. Optim. 27(1): 110-123 (2017) - [c4]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Approximate Steepest Coordinate Descent. ICML 2017: 3251-3259 - [c3]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Safe Adaptive Importance Sampling. NIPS 2017: 4381-4391 - [i5]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Approximate Steepest Coordinate Descent. CoRR abs/1706.08427 (2017) - [i4]Sebastian U. Stich, Anant Raj, Martin Jaggi:
Safe Adaptive Importance Sampling. CoRR abs/1711.02637 (2017) - 2016
- [j4]Sebastian U. Stich, Christian L. Müller, Bernd Gärtner:
Variable metric random pursuit. Math. Program. 156(1-2): 549-579 (2016) - [j3]Hemant Tyagi, Sebastian U. Stich, Bernd Gärtner:
On Two Continuum Armed Bandit Problems in High Dimensions. Theory Comput. Syst. 58(1): 191-222 (2016) - 2014
- [b1]Sebastian U. Stich:
Convex Optimization with Random Pursuit. ETH Zurich, Zürich, Switzerland, 2014 - [c2]Sebastian Urban Stich:
On Low Complexity Acceleration Techniques for Randomized Optimization. PPSN 2014: 130-140 - [i3]Sebastian U. Stich:
On low complexity Acceleration Techniques for Randomized Optimization: Supplementary Online Material. CoRR abs/1406.2010 (2014) - 2013
- [j2]Sebastian U. Stich, Christian L. Müller, Bernd Gärtner:
Optimization of Convex Functions with Random Pursuit. SIAM J. Optim. 23(2): 1284-1309 (2013) - [i2]Hemant Tyagi, Sebastian U. Stich, Bernd Gärtner:
Stochastic continuum armed bandit problem of few linear parameters in high dimensions. CoRR abs/1312.0232 (2013) - 2012
- [c1]Sebastian U. Stich, Christian L. Müller:
On Spectral Invariance of Randomized Hessian and Covariance Matrix Adaptation Schemes. PPSN (1) 2012: 448-457 - 2011
- [i1]Sebastian U. Stich, Christian L. Müller, Bernd Gärtner:
Optimization of Convex Functions with Random Pursuit. CoRR abs/1111.0194 (2011)
2000 – 2009
- 2009
- [j1]Dan Hefetz, Sebastian U. Stich:
On Two Problems Regarding the Hamiltonian Cycle Game. Electron. J. Comb. 16(1) (2009)
Coauthor Index
[c43] [c40] [c37] [i55] [c35] [c31] [i49] [i45] [i44] [i43] [j7] [c30] [c29] [c28] [c27] [c26] [c25] [c24] [c22] [c21] [i41] [i40] [i39] [i37] [i36] [i35] [i34] [i33] [i31] [c20] [c19] [c18] [c16] [c15] [c13] [i28] [i27] [i26] [i25] [i23] [i21] [c12] [c10] [c9] [i20] [i19] [i17] [i14] [c8] [c7] [c5] [i12] [i9] [i8] [i7] [i6] [c4] [c3] [i5] [i4]
Sai Praneeth Karimireddy
aka: Sai Praneeth Reddy Karimireddy
aka: Sai Praneeth Reddy Karimireddy
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Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-21 00:22 CET by the dblp team
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