Samuel Horváth
Orcid: 0000-0003-0619-9260Affiliations:
- Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Masdar City, UAE
- King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
According to our database1,
Samuel Horváth
authored at least 69 papers
between 2019 and 2025.
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Bibliography
2025
Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level.
CoRR, July, 2025
DES-LOC: Desynced Low Communication Adaptive Optimizers for Training Foundation Models.
CoRR, May, 2025
Convergence of Clipped-SGD for Convex (L<sub>0</sub>,L<sub>1</sub>)-Smooth Optimization with Heavy-Tailed Noise.
CoRR, May, 2025
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy.
CoRR, February, 2025
CoRR, February, 2025
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning.
Trans. Mach. Learn. Res., 2025
Trans. Mach. Learn. Res., 2025
Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
Global-QSGD: Allreduce-Compatible Quantization for Distributed Learning with Theoretical Guarantees.
Proceedings of the 5th Workshop on Machine Learning and Systems, 2025
Proceedings of the 5th Workshop on Machine Learning and Systems, 2025
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning.
Proceedings of the Conference on Parsimony and Learning, 2025
Proceedings of the Conference on Parsimony and Learning, 2025
Proceedings of the Conference on Parsimony and Learning, 2025
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025
2024
Trans. Mach. Learn. Res., 2024
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning.
CoRR, 2024
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training.
CoRR, 2024
Methods for Convex (L<sub>0</sub>,L<sub>1</sub>)-Smooth Optimization: Clipping, Acceleration, and Adaptivity.
CoRR, 2024
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Proceedings of the Forty-first International Conference on Machine Learning, 2024
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Low-Resource Machine Translation through the Lens of Personalized Federated Learning.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024
2023
Stochastic distributed learning with gradient quantization and double-variance reduction.
Optim. Methods Softw., January, 2023
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences.
CoRR, 2023
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees.
CoRR, 2023
CoRR, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance.
Proceedings of the International Conference on Machine Learning, 2023
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity.
Proceedings of the International Conference on Machine Learning, 2023
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity.
PhD thesis, 2022
Trans. Mach. Learn. Res., 2022
SIAM J. Math. Data Sci., 2022
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity.
CoRR, 2022
Proceedings of the Mathematical and Scientific Machine Learning, 2022
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021
Proceedings of the DistributedML '21: Proceedings of the 2nd ACM International Workshop on Distributed Machine Learning, 2021
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop.
Proceedings of the Algorithmic Learning Theory, 2020
2019
Proceedings of the 36th International Conference on Machine Learning, 2019