2 citations · 4 across the 24 of their papers we have counts for
6 papers · 1 filter
Tractable Probabilistic Models for Investment Planning
Nicolas M. Cuadrado A., Mohannad Takrouri, Jiří Němeček +2
Investment planning in power utilities, such as generation and transmission expansion, requires decisions under substantial uncertainty over decade--long horizons for policies, dem…
Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update
Abdulla Jasem Almansoori, Maria Ivanova, Andrey Veprikov +3
Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights, dramatically reducing trainable parameters and memory. However, there is s…
Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
Artem Agafonov, Vladislav Ryspayev, Samuel Horváth +3
Quasi-Newton methods are widely used for solving convex optimization problems due to their ease of implementation, practical efficiency, and strong local convergence guarantees. Ho…
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
Nurbek Tastan, Stefanos Laskaridis, Martin Takac +2
Large pre-trained models are commonly adapted to downstream tasks using parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA), which injects small trainable lo…
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…
Knowledge Distillation from Large Language Models for Household Energy Modeling
Mohannad Takrouri, Nicolás M. Cuadrado, Martin Takáč
Machine learning (ML) is increasingly vital for smart-grid research, yet restricted access to realistic, diverse data - often due to privacy concerns - slows progress and fuels dou…