6 papers
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…
UCB-type Algorithm for Budget-Constrained Expert Learning
Ilgam Latypov, Alexandra Suvorikova, Alexey Kroshnin +2
In many modern applications, a system must dynamically choose between several adaptive learning algorithms that are trained online. Examples include model selection in streaming en…
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…
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…