3 citations · 3 across the 11 of their papers we have counts for
11 papers
Controlling Refusal Behavior of LLMs via Stiefel-Constrained Rotation Steering
Kirill Bunin, Dmitry Bylinkin, Vladimir Aletov +3
Activation steering has emerged as a lightweight approach for controlling model refusal at inference time. A growing line of research explores trainable rotations of activations to…
Leveraging Association Context Retrieval in Knowledge Edit- ing to Build White-Box Attacks on LLMs
Roman Maksimov, Vladimir Aletov, Vladimir Solodkin +3
As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a novel white-box attack inspire…
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…
Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
Roman Maksimov, Vladimir Aletov, Dmitry Bylinkin +3
Knowledge editing (KE) provides a lightweight alternative to repeated fine-tuning of LLMs. However, most existing KE methods target dense feed-forward layers, while modern LLMs inc…
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
Daniil Medyakov, Gleb Molodtsov, Grigoriy Evseev +2
Variational inequalities have gained significant attention in machine learning and optimization research. While stochastic methods for solving these problems typically assume indep…
Communication-Efficient Federated Learning with Adaptive Number of Participants
Sergey Skorik, Vladislav Dorofeev, Gleb Molodtsov +4
Rapid scaling of deep learning models has enabled performance gains across domains, yet it introduced several challenges. Federated Learning (FL) has emerged as a promising framewo…
Sign-SGD via Parameter-Free Optimization
Daniil Medyakov, Sergey Stanko, Gleb Molodtsov +4
Large language models have achieved major advances across domains, yet training them remains extremely resource-intensive. We revisit Sign-SGD, which serves both as a memory-effici…
Bant: Byzantine Antidote via Trial Function and Trust Scores
Gleb Molodtsov, Daniil Medyakov, Sergey Skorik +6
Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their st…
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Daniil Medyakov, Gleb Molodtsov, Savelii Chezhegov +2
Stochastic optimization algorithms are widely used for machine learning with large-scale data. However, their convergence often suffers from non-vanishing variance. Variance Reduct…
Effective Method with Compression for Distributed and Federated Cocoercive Variational Inequalities
Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov
Variational inequalities as an effective tool for solving applied problems, including machine learning tasks, have been attracting more and more attention from researchers in recen…
Optimal Data Splitting in Distributed Optimization for Machine Learning
Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov +1
The distributed optimization problem has become increasingly relevant recently. It has a lot of advantages such as processing a large amount of data in less time compared to non-di…