Samuel Horváth
30 papers · 2019–2026 · 9 venues · across top CS/AI venues
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🗺️ Taxonomy Completionist (43) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Deep Specialist
(17)
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Dynamic Duo
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Prolific Year · Productive
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The Questioner
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Century Club · Bronze
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Research topics
Machine Learning
(28)
Optimization & Theory · Machine Learning
(23)
Artificial Intelligence
(15)
Learning Paradigms · Artificial Intelligence
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Mathematics & Optimization
(10)
Learning Paradigms · Machine Learning
(10)
Optimization
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Application Areas
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Deep Learning
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Core AI
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Learning Types · Machine Learning
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Learning Types · Deep Learning
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Optimization & Theory · Deep Learning
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Techniques
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Natural Language Processing
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Keywords
federated learning
(11)
variance reduction
(4)
data heterogeneity
(4)
stochastic optimization
(4)
distributed optimization
(4)
gradient clipping
(4)
communication compression
(4)
variational inequality
(3)
non-convex optimization
(3)
stochastic gradient
(3)
byzantine robustness
(3)
personalized federated learning
(3)
distributed learning
(3)
uncertainty quantification
(2)
stochastic gradient descent
(2)
error feedback
(2)
contribution assessment
(2)
convergence guarantee
(2)
knowledge distillation
(2)
convergence rate
(2)
Papers
Who to Trust? Aggregating Client Predictions in Federated Distillation.
UAI 2026
Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks
ICML 2025
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
ICML 2025
DPFL: Decentralized Personalized Federated Learning
AISTATS 2025
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training
ICML 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
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
AISTATS 2025
Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
NeurIPS 2024
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks
IJCAI 2024
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
Low-Resource Machine Translation through the Lens of Personalized Federated Learning
EMNLP 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
NeurIPS 2024
Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance
NeurIPS 2023
Byzantine-Tolerant Methods for Distributed Variational Inequalities
NeurIPS 2023
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
ICML 2023
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
NeurIPS 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
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
ICLR 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
NeurIPS 2021
Hyperparameter Transfer Learning with Adaptive Complexity
AISTATS 2021
Don’t Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop
ALT 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning
NeurIPS 2020
Nonconvex Variance Reduced Optimization with Arbitrary Sampling
ICML 2019