3 papers
cs.LG2026
Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning
Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin +1
Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specifi…
math.OC2026
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
Dmitry Bylinkin, Sergey Skorik, Dmitriy Bystrov +3
Heterogeneity within data distribution poses a challenge in many modern federated learning tasks. We formalize it as an optimization problem involving a computationally heavy compo…
cs.LG2024
TQCompressor: improving tensor decomposition methods in neural networks via permutations
V. Abronin, A. Naumov, D. Mazur +7
We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage…