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Vera Soboleva

4 accepted papers

2026

LoRA meets Riemannion: Muon Optimizer for Parametrization-independent Low-Rank Adapters

ICLR 2026poster

This work presents a novel, fully Riemannian framework for Low-Rank Adaptation (LoRA) that geometrically treats low-rank adapters by optimizing them directly on the fixed-rank manifold. This formulation eliminates the parametrization ambiguity present in standard Euclidean optimizers. Our framework…

Cited by 0SourceScholar
2026

OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models

CVPR 2026

In a rapidly growing field of model training there is a constant practical interest in parameter-efficient fine-tuning and various techniques that use a small amount of training data to adapt the model to a narrow task. However, there is an open question: how to combine several adapters tuned for di

Cited by 0SourcecodeScholar
2026

T-LoRA: Single Image Diffusion Model Customization Without Overfitting

AAAI 2026technical

While diffusion model fine-tuning offers a powerful approach for customizing pre-trained models to generate specific objects, it frequently suffers from overfitting when training samples are limited, compromising both generalization capability and output diversity. This paper tackles the challenging

Cited by 11SourcePDFScholar
2024

Group and Shuffle: Efficient Structured Orthogonal Parametrization

NeurIPS 2024poster

The increasing size of neural networks has led to a growing demand for methods of efficient finetuning. Recently, an orthogonal finetuning paradigm was introduced that uses orthogonal matrices for adapting the weights of a pretrained model. In this paper, we introduce a new class of structured matri…

Cited by 10SourcePDFScholar