1 citations · 2 across the 5 of their papers we have counts for
6 papers
Building a Nested LADDER for LLM Unstructured Post-Training Pruning
Nicolás Mauricio Cuadrado Ávila, Ivo Gollini Navarrete, Martin Takáč +1
Non-uniform allocation can improve post-training pruning of large language models, yet quality can still degrade sharply at high sparsity. To control degradation as sparsity increa…
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
Ruichen Luo, Sebastian U Stich, Samuel Horváth +1
LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated lea…
What Scalable Second-Order Information Knows for Pruning at Initialization
Ivo Gollini Navarrete, Nicolás Mauricio Cuadrado Ávila, Martin Takáč +1
Pruning remains an effective strategy for reducing both the costs and environmental impact associated with deploying large neural networks (NNs) while maintaining performance. Clas…
Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL
Nicolas M Cuadrado Avila, Samuel Horváth, Martin Takáč
This work studies the challenge of optimal energy management in building-based microgrids through a collaborative and privacy-preserving framework. We evaluated two common RL algor…
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky +4
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic gene…
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
Sara Pieri, Jose Renato Restom, Samuel Horvath +1
Federated Learning (FL) is a promising research paradigm that enables the collaborative training of machine learning models among various parties without the need for sensitive inf…