91 citations · 113 across the 41 of their papers we have counts for
9 papers · 1 filter
Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL
Nicolas M Cuadrado Avila, Samuel Horváth, Martin Takáč
This work studies the challenge of optimal energy management in building-based microgrids through a collaborative and privacy-preserving framework. We evaluated two common RL algor…
Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under -Smoothness
Aleksandr Lobanov, Alexander Gasnikov, Eduard Gorbunov +1
The gradient descent (GD) method -- is a fundamental and likely the most popular optimization algorithm in machine learning (ML), with a history traced back to a paper in 1847 (Cau…
Methods for Convex -Smooth Optimization: Clipping, Acceleration, and Adaptivity
Eduard Gorbunov, Nazarii Tupitsa, Sayantan Choudhury +4
Due to the non-smoothness of optimization problems in Machine Learning, generalized smoothness assumptions have been gaining a lot of attention in recent years. One of the most pop…
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
Samar Fares, Klea Ziu, Toluwani Aremu +5
Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses. To…
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
Savelii Chezhegov, Yaroslav Klyukin, Andrei Semenov +5
Methods with adaptive stepsizes, such as AdaGrad and Adam, are essential for training modern Deep Learning models, especially Large Language Models. Typically, the noise in the sto…
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
Artem Agafonov, Petr Ostroukhov, Roman Mozhaev +5
Variational inequalities represent a broad class of problems, including minimization and min-max problems, commonly found in machine learning. Existing second-order and high-order…
Newton Method Revisited: Global Convergence Rates up to for Stepsize Schedules and Linesearch Procedures
Slavomír Hanzely, Farshed Abdukhakimov, Martin Takáč
This paper investigates the global convergence of stepsized Newton methods for convex functions with Hölder continuous Hessians or third derivatives. We propose several simple step…
Enhancing Policy Gradient with the Polyak Step-Size Adaption
Yunxiang Li, Rui Yuan, Chen Fan +4
Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL). Renowned for its convergence guarantees and stability compared to other…
Federated Learning Can Find Friends That Are Advantageous
Nazarii Tupitsa, Samuel Horváth, Martin Takáč +1
In Federated Learning (FL), the distributed nature and heterogeneity of client data present both opportunities and challenges. While collaboration among clients can significantly e…