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Samuel Horváth
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- affiliation: King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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2020 – today
- 2024
- [j6]Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác:
PaDPaF: Partial Disentanglement with Partially-Federated GANs. Trans. Mach. Learn. Res. 2024 (2024) - [c18]Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov:
Efficient Conformal Prediction under Data Heterogeneity. AISTATS 2024: 4879-4887 - [c17]Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik:
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. ICML 2024 - [c16]Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang:
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. ICML 2024 - [i40]Nazarii Tupitsa, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Federated Learning Can Find Friends That Are Beneficial. CoRR abs/2402.05050 (2024) - [i39]Mohammed Aljahdali, Ahmed M. Abdelmoniem, Marco Canini
, Samuel Horváth:
Flashback: Understanding and Mitigating Forgetting in Federated Learning. CoRR abs/2402.05558 (2024) - [i38]Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad. CoRR abs/2403.02648 (2024) - [i37]Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth, Martin Takác:
Generalized Policy Learning for Smart Grids: FL TRPO Approach. CoRR abs/2403.18439 (2024) - [i36]Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt, Samuel Horváth, Robert M. Gower, Martin Takác:
Enhancing Policy Gradient with the Polyak Step-Size Adaption. CoRR abs/2404.07525 (2024) - [i35]Nurbek Tastan, Samar Fares, Toluwani Aremu, Samuel Horváth, Karthik Nandakumar:
Redefining Contributions: Shapley-Driven Federated Learning. CoRR abs/2406.00569 (2024) - [i34]Saveliy Chezhegov, Yaroslav Klyukin, Andrei Semenov, Aleksandr Beznosikov, Alexander V. Gasnikov, Samuel Horváth, Martin Takác, Eduard Gorbunov:
Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed. CoRR abs/2406.04443 (2024) - [i33]Salma Kharrat, Marco Canini, Samuel Horváth:
Decentralized Personalized Federated Learning. CoRR abs/2406.06520 (2024) - [i32]Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horváth, Eduard Gorbunov, Irina Nikishina:
Low-Resource Machine Translation through the Lens of Personalized Federated Learning. CoRR abs/2406.12564 (2024) - 2023
- [j5]Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan:
On Biased Compression for Distributed Learning. J. Mach. Learn. Res. 24: 276:1-276:50 (2023) - [j4]Samuel Horváth
Stochastic distributed learning with gradient quantization and double-variance reduction. Optim. Methods Softw. 38(1): 91-106 (2023) - [c15]Eduard Gorbunov, Samuel Horváth, Peter Richtárik, Gauthier Gidel:
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. ICLR 2023 - [c14]Eduard Gorbunov, Adrien B. Taylor, Samuel Horváth, Gauthier Gidel:
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity. ICML 2023: 11614-11641 - [c13]Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik:
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. ICML 2023: 29563-29648 - [c12]Nikita Kornilov, Ohad Shamir, Aleksandr V. Lobanov, Darina Dvinskikh, Alexander V. Gasnikov, Innokentiy Shibaev, Eduard Gorbunov, Samuel Horváth:
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance. NeurIPS 2023 - [c11]Sara Pieri, Jose Renato Restom, Samuel Horváth, Hisham Cholakkal:
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. NeurIPS 2023 - [c10]Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov:
Byzantine-Tolerant Methods for Distributed Variational Inequalities. NeurIPS 2023 - [i31]Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik:
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. CoRR abs/2302.00999 (2023) - [i30]Grigory Malinovsky
Federated Learning with Regularized Client Participation. CoRR abs/2302.03662 (2023) - [i29]Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horváth
Improving Performance of Private Federated Models in Medical Image Analysis. CoRR abs/2304.05127 (2023) - [i28]Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth:
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. CoRR abs/2305.18285 (2023) - [i27]Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth:
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees. CoRR abs/2305.18627 (2023) - [i26]Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov, Fakhri Karray, Peter Richtárik:
Clip21: Error Feedback for Gradient Clipping. CoRR abs/2305.18929 (2023) - [i25]Samuel Horváth
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. CoRR abs/2308.14929 (2023) - [i24]Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise. CoRR abs/2310.01860 (2023) - [i23]Sara Pieri, Jose Renato Restom, Samuel Horváth
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. CoRR abs/2310.15165 (2023) - [i22]Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov:
Byzantine-Tolerant Methods for Distributed Variational Inequalities. CoRR abs/2311.04611 (2023) - [i21]Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov:
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. CoRR abs/2311.14127 (2023) - [i20]Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov:
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR abs/2312.11230 (2023) - [i19]Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov:
Efficient Conformal Prediction under Data Heterogeneity. CoRR abs/2312.15799 (2023) - 2022
- [b1]Samuel Horváth
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2022 - [j3]Samuel Horváth, Lihua Lei, Peter Richtárik
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. 4(2): 634-648 (2022) - [j2]Wenlin Chen
Optimal Client Sampling for Federated Learning. Trans. Mach. Learn. Res. 2022 (2022) - [j1]Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael G. Rabbat:
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. Trans. Mach. Learn. Res. 2022 (2022) - [c9]Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik:
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. AISTATS 2022: 11374-11421 - [c8]Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik:
Natural Compression for Distributed Deep Learning. MSML 2022: 129-141 - [i18]Konstantin Burlachenko, Samuel Horváth
FL_PyTorch: optimization research simulator for federated learning. CoRR abs/2202.03099 (2022) - [i17]Samuel Horváth
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR abs/2204.13169 (2022) - [i16]Eduard Gorbunov, Samuel Horváth
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. CoRR abs/2206.00529 (2022) - [i15]Samuel Horváth
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity. CoRR abs/2207.00392 (2022) - [i14]Samuel Horváth
Granger Causality using Neural Networks. CoRR abs/2208.03703 (2022) - [i13]Samuel Horváth
Adaptive Learning Rates for Faster Stochastic Gradient Methods. CoRR abs/2208.05287 (2022) - [i12]Abdulla Jasem Almansoori, Samuel Horváth
Partial Disentanglement with Partially-Federated GANs (PaDPaF). CoRR abs/2212.03836 (2022) - 2021
- [c7]Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau:
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS 2021: 1378-1386 - [c6]Konstantin Burlachenko, Samuel Horváth
FL_PyTorch: optimization research simulator for federated learning. DistributedML@CoNEXT 2021: 1-7 - [c5]Samuel Horváth, Peter Richtárik:
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. ICLR 2021 - [c4]Samuel Horváth, Stefanos Laskaridis, Mário Almeida, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane:
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. NeurIPS 2021: 12876-12889 - [i11]Samuel Horváth
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR abs/2102.12810 (2021) - [i10]Samuel Horváth
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. CoRR abs/2102.13451 (2021) - [i9]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
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i8]Elnur Gasanov, Ahmed Khaled, Samuel Horváth
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. CoRR abs/2111.11556 (2021) - 2020
- [c3]Dmitry Kovalev, Samuel Horváth, Peter Richtárik:
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. ALT 2020: 451-467 - [c2]Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik:
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. NeurIPS 2020 - [i7]Samuel Horváth, Lihua Lei, Peter Richtárik
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR abs/2002.05359 (2020) - [i6]Aleksandr Beznosikov
On Biased Compression for Distributed Learning. CoRR abs/2002.12410 (2020) - [i5]Samuel Horváth
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. CoRR abs/2006.11077 (2020) - [i4]Filip Hanzely
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. CoRR abs/2010.02372 (2020) - [i3]Wenlin Chen, Samuel Horváth
Optimal Client Sampling for Federated Learning. CoRR abs/2010.13723 (2020)
2010 – 2019
- 2019
- [c1]Samuel Horváth, Peter Richtárik:
Nonconvex Variance Reduced Optimization with Arbitrary Sampling. ICML 2019: 2781-2789 - [i2]Dmitry Kovalev
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop. CoRR abs/1901.08689 (2019) - [i1]Samuel Horváth
Natural Compression for Distributed Deep Learning. CoRR abs/1905.10988 (2019)
Coauthor Index
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last updated on 2024-09-14 01:12 CEST by the dblp team
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