Risultati della ricerca - Horvath, Samuel

Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity di Horváth, Samuel

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A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning di Horváth, Samuel, Richtárik, Peter

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Vanishing Feature: Diagnosing Model Merging and Beyond di Qu, Xingyu, Horvath, Samuel

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Optimal Client Sampling for Federated Learning di Chen, Wenlin, Horvath, Samuel, Richtarik, Peter

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FL_PyTorch: optimization research simulator for federated learning di Burlachenko, Konstantin, Horváth, Samuel, Richtárik, Peter

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Adaptive Learning Rates for Faster Stochastic Gradient Methods di Horváth, Samuel, Mishchenko, Konstantin, Richtárik, Peter

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Decentralized Personalized Federated Learning di Kharrat, Salma, Canini, Marco, Horvath, Samuel

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CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning di Tastan, Nurbek, Horvath, Samuel, Nandakumar, Karthik

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks di Tastan, Nurbek, Horvath, Samuel, Nandakumar, Karthik

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Stochastic Self-Organization in Multi-Agent Systems di Tastan, Nurbek, Horvath, Samuel, Nandakumar, Karthik

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Can Muon Fine-tune Adam-Pretrained Models? di Qu, Xingyu, Huang, Peigeng, Horvath, Samuel

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Granger Causality using Neural Networks di Sultan, Malik Shahid, Horvath, Samuel, Ombao, Hernando

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PaDPaF: Partial Disentanglement with Partially-Federated GANs di Almansoori, Abdulla Jasem, Horváth, Samuel, Takáč, Martin

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Collaborative and Efficient Personalization with Mixtures of Adaptors di Almansoori, Abdulla Jasem, Horváth, Samuel, Takáč, Martin

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Learning in the Null Space: Small Singular Values for Continual Learning di Pham, Cuong Anh, Vepakomma, Praneeth, Horváth, Samuel

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On Biased Compression for Distributed Learning di Beznosikov, Aleksandr, Horváth, Samuel, Richtárik, Peter, Safaryan, Mher

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Lower Bounds and Optimal Algorithms for Personalized Federated Learning di Hanzely, Filip, Hanzely, Slavomír, Horváth, Samuel, Richtárik, Peter

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Hyperparameter Transfer Learning with Adaptive Complexity di Horváth, Samuel, Klein, Aaron, Richtárik, Peter, Archambeau, Cédric

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FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning di Gasanov, Elnur, Khaled, Ahmed, Horváth, Samuel, Richtárik, Peter

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Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top di Gorbunov, Eduard, Horváth, Samuel, Richtárik, Peter, Gidel, Gauthier

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