6 citations · 6 across the 12 of their papers we have counts for
12 papers
SDG-MoE: Signed Debate Graph Mixture-of-Experts
Stepan Kulibaba, Kirill Labzin, Artem Dzhalilov +4
Sparse MoE models achieve a good balance between capacity and compute by routing each token to a small subset of experts. However, in most MoE architectures, once a token is routed…
SCOPE: Smooth Convex Optimization for Planned Evolution of Deformable Linear Objects
Ali Jnadi, Hadi Salloum, Yaroslav Kholodov +2
We present SCOPE, a fast and efficient framework for modeling and manipulating deformable linear objects (DLOs). Unlike conventional energy-based approaches, SCOPE leverages convex…
Quantum-Inspired Episode Selection for Monte Carlo Reinforcement Learning via QUBO Optimization
Hadi Salloum, Ali Jnadi, Yaroslav Kholodov +1
Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and correlated trajectories. We add…
Training-Free Out-Of-Distribution Segmentation With Foundation Models
Laith Nayal, Hadi Salloum, Ahmad Taha +2
Detecting unknown objects in semantic segmentation is crucial for safety-critical applications such as autonomous driving. Large vision foundation models, including DINOv2, InternI…
Managing Self-Learning Experts under Per-Round Budget Constraints
Ilgam Latypov, Alexandra Suvorikova, Alexey Kroshnin +2
This paper addresses the problem of sequential decision-making under learning budget constraints. Such settings naturally arise in applications like managing a portfolio of bandit…
AdLoCo: adaptive batching significantly improves communications efficiency and convergence for Large Language Models
Nikolay Kutuzov, Makar Baderko, Stepan Kulibaba +4
Scaling distributed training of Large Language Models (LLMs) requires not only algorithmic advances but also efficient utilization of heterogeneous hardware resources. While existi…
KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems
Stepan Kulibaba, Artem Dzhalilov, Roman Pakhomov +3
Recent Large Language Model (LLM)-based AutoML systems demonstrate impressive capabilities but face significant limitations such as constrained exploration strategies and a severe…
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under -Smoothness
Nikita Kornilov, Philip Zmushko, Andrei Semenov +3
In recent years, non-convex optimization problems are more often described by generalized -smoothness assumption rather than standard one. Meanwhile, severely corrupted…
Exploring Applications of State Space Models and Advanced Training Techniques in Sequential Recommendations: A Comparative Study on Efficiency and Performance
Mark Obozov, Makar Baderko, Stepan Kulibaba +2
Recommender systems aim to estimate the dynamically changing user preferences and sequential dependencies between historical user behaviour and metadata. Although transformer-based…
Optimal Flow Matching: Learning Straight Trajectories in Just One Step
Nikita Kornilov, Petr Mokrov, Alexander Gasnikov +1
Over the several recent years, there has been a boom in development of Flow Matching (FM) methods for generative modeling. One intriguing property pursued by the community is the a…
Activations and Gradients Compression for Model-Parallel Training
Mikhail Rudakov, Aleksandr Beznosikov, Yaroslav Kholodov +1
Large neural networks require enormous computational clusters of machines. Model-parallel training, when the model architecture is partitioned sequentially between workers, is a po…
Efficient randomized algorithms for PageRank problem
Alexander Gasnikov, Denis Dmitriev
In the paper we compare well known numerical methods of finding PageRank vector. We propose Markov Chain Monte Carlo method and obtain a new estimation for this method. We also pro…