Samuel Horváth

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 55

Top Topics

Gradient Method

Granger Causality

Stochastic Gradient

Visual Recognition

Top Venues

Publications

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 (2024)
Nurbek Tastan, Samar Fares, Toluwani Aremu, Samuel Horváth, Karthik Nandakumar
Redefining Contributions: Shapley-Driven Federated Learning. CoRR (2024)
Nazarii Tupitsa, Samuel Horváth, Martin Takác, Eduard Gorbunov
Federated Learning Can Find Friends That Are Beneficial. CoRR (2024)
Mohammed Aljahdali, Ahmed M. Abdelmoniem, Marco Canini, Samuel Horváth
Flashback: Understanding and Mitigating Forgetting in Federated Learning. CoRR (2024)
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)
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 (2024)
Viktor Moskvoretskii, Nazarii Tupitsa, Chris Biemann, Samuel Horváth, Eduard Gorbunov, Irina Nikishina
Low-Resource Machine Translation through the Lens of Personalized Federated Learning. CoRR (2024)
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 (2024)
Salma Kharrat, Marco Canini, Samuel Horváth
Decentralized Personalized Federated Learning. CoRR (2024)
Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác
PaDPaF: Partial Disentanglement with Partially-Federated GANs. Trans. Mach. Learn. Res. 2024 (2024)
Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth, Martin Takác
Generalized Policy Learning for Smart Grids: FL TRPO Approach. CoRR (2024)
Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. CoRR (2023)
Nikita Kotelevskii, Samuel Horváth, Karthik Nandakumar, Martin Takác, Maxim Panov
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. CoRR (2023)
Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horváth
Improving Performance of Private Federated Models in Medical Image Analysis. CoRR (2023)
Sara Pieri, Jose Renato Restom, Samuel Horváth, Hisham Cholakkal
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. NeurIPS (2023)
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)
Samuel Horváth, Stefanos Laskaridis, Shashank Rajput, Hongyi Wang
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition. CoRR (2023)
Grigory Malinovsky, Peter Richtárik, Samuel Horváth, Eduard Gorbunov
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences. CoRR (2023)
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)
Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov
Byzantine-Tolerant Methods for Distributed Variational Inequalities. CoRR (2023)
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)
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. CoRR (2023)
Nazarii Tupitsa, Abdulla Jasem Almansoori, Yanlin Wu, Martin Takác, Karthik Nandakumar, Samuel Horváth, Eduard Gorbunov
Byzantine-Tolerant Methods for Distributed Variational Inequalities. NeurIPS (2023)
Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik
Federated Learning with Regularized Client Participation. CoRR (2023)
Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth
Global-QSGD: Practical Floatless Quantization for Distributed Learning with Theoretical Guarantees. CoRR (2023)
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 (2023)
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) (2023)
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 (2023)
Sarit Khirirat, Eduard Gorbunov, Samuel Horváth, Rustem Islamov, Fakhri Karray, Peter Richtárik
Clip21: Error Feedback for Gradient Clipping. CoRR (2023)
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)
Wenlin Chen, Samuel Horváth, Peter Richtárik
Optimal Client Sampling for Federated Learning. Trans. Mach. Learn. Res. 2022 (2022)
Samuel Horváth, Malik Shahid Sultan, Hernando Ombao
Granger Causality using Neural Networks. CoRR (2022)
Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác
Partial Disentanglement with Partially-Federated GANs (PaDPaF). CoRR (2022)
Samuel Horváth, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, Peter Richtárik
Natural Compression for Distributed Deep Learning. MSML (2022)
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)
Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. AISTATS (2022)
Samuel Horváth
Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity. CoRR (2022)
Samuel Horváth, Maziar Sanjabi, Lin Xiao, Peter Richtárik, Michael Rabbat
FedShuffle: Recipes for Better Use of Local Work in Federated Learning. CoRR (2022)
Samuel Horváth, Konstantin Mishchenko, Peter Richtárik
Adaptive Learning Rates for Faster Stochastic Gradient Methods. CoRR (2022)
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
FL_PyTorch: optimization research simulator for federated learning. CoRR (2022)
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. CoRR (2022)
Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. SIAM J. Math. Data Sci. 4 (2) (2022)
Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
Hyperparameter Transfer Learning with Adaptive Complexity. CoRR (2021)
Samuel Horváth, Peter Richtárik
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. ICLR (2021)
Elnur Gasanov, Ahmed Khaled, Samuel Horváth, Peter Richtárik
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. CoRR (2021)
Konstantin Burlachenko, Samuel Horváth, Peter Richtárik
FL_PyTorch: optimization research simulator for federated learning. DistributedML@CoNEXT (2021)
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)
Samuel Horváth, Aaron Klein, Peter Richtárik, Cédric Archambeau
Hyperparameter Transfer Learning with Adaptive Complexity. AISTATS (2021)
Samuel Horváth, Peter Richtárik
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. CoRR (2020)
Samuel Horváth, Lihua Lei, Peter Richtárik, Michael I. Jordan
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization. CoRR (2020)
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)
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. CoRR (2020)
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtárik
Lower Bounds and Optimal Algorithms for Personalized Federated Learning. NeurIPS (2020)
Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, Mher Safaryan
On Biased Compression for Distributed Learning. CoRR (2020)
Samuel Horváth, Peter Richtárik
Nonconvex Variance Reduced Optimization with Arbitrary Sampling. ICML (2019)