← Authors

Samuel Horváth

29 papers · 2019–2025 · 8 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (8) 🏃 Academic Marathon (6) 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🐝 Cross-Pollinator (9) 🗺️ Taxonomy Completionist (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🤝 Dynamic Duo (13) 👑 Triple Crown 🔥 Unstoppable (7) 💎 Century Club (29) ⚡ Prolific Year (8) 🗃️ Keyword Collector (79)

Conferences

ICML (8) NIPS (7) AISTATS (5) ICLR (4) IJCAI (2) ALT (1) EMNLP (1) JMLR (1)

Top co-authors

Peter Richtarik (13) Eduard Gorbunov (12) Martin Takac (9) Karthik Nandakumar (4) Gauthier Gidel (4) Nazarii Tupitsa (4) Alexander Gasnikov (4) Aleksandr Beznosikov (3) Maxim Panov (2) Pavel Dvurechensky (2)

Keywords

federated learning (8) variance reduction (3) personalized federated learning (3) stochastic optimization (3) variational inequality (3) uncertainty quantification (2) data heterogeneity (2) convergence guarantee (2) non-convex optimization (2) distributed learning (2) stochastic gradient (2) byzantine robustness (2) distributed optimization (2) communication compression (2) sample efficiency (1) convex optimization (1) transfer learning (1) stochastic gradient descent (1) conformal prediction (1) communication complexity (1)

Papers

Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed ICML 2025
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks ICML 2025
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training ICML 2025
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis AISTATS 2025
DPFL: Decentralized Personalized Federated Learning AISTATS 2025
Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity ICLR 2025
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization ICLR 2025
Efficient Conformal Prediction under Data Heterogeneity AISTATS 2024
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise ICML 2024
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition ICML 2024
Redefining Contributions: Shapley-Driven Federated Learning IJCAI 2024
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad NIPS 2024
Low-Resource Machine Translation through the Lens of Personalized Federated Learning EMNLP 2024
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences NIPS 2024
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks IJCAI 2024
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top ICLR 2023
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity ICML 2023
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance ICML 2023
On Biased Compression for Distributed Learning JMLR 2023
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition NIPS 2023
Byzantine-Tolerant Methods for Distributed Variational Inequalities NIPS 2023
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance NIPS 2023
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning AISTATS 2022
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning ICLR 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout NIPS 2021
Hyperparameter Transfer Learning with Adaptive Complexity AISTATS 2021
Lower Bounds and Optimal Algorithms for Personalized Federated Learning NIPS 2020
Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop ALT 2020
Nonconvex Variance Reduced Optimization with Arbitrary Sampling ICML 2019