activity
20242026
most citedAccelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks

1 citations · 1 across the 5 of their papers we have counts for

collaborators

9 papers

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

math.OC2026

Markovian Compression: Looking to the Past Helps Accelerate the Future

Andrey Veprikov, Vladimir Solodkin, Mikhail Rudakov +2

This paper deals with distributed optimization problems that use compressed communication to achieve efficient performance and mitigate communication bottleneck. We propose a famil…

cs.LG2025

WeightLoRA: Keep Only Necessary Adapters

Andrey Veprikov, Vladimir Solodkin, Alexander Zyl +2

The widespread utilization of language models in modern applications is inconceivable without Parameter-Efficient Fine-Tuning techniques, such as low-rank adaptation ($\texttt{LoRA…

math.OC2024

Methods for Solving Variational Inequalities with Markovian Stochasticity

Vladimir Solodkin, Michael Ermoshin, Roman Gavrilenko +1

In this paper, we present a novel stochastic method for solving variational inequalities (VI) in the context of Markovian noise. By leveraging Extragradient technique, we can produ…