Posts

“Adaptive Regularized Newton Method with Inexact Hessian” – joint work with Aleksandr Shestakov, Nail Bashirov, Alexander Gasnikov, Martin Takáč, Aleksandr Beznosikov and Dmitry Kamzolov.

Between December 2 and December 7, I will attend EurIPS 2025 in Copenhagen, an event officially endorsed by NeurIPS. I will present two papers there: “Benchmarking Optimizers for Large Language Model Pretraining” and “Just a Simple Transformation is Enough for Data Protection in Split Learning”.

“Adaptive Regularized Newton Method with Inexact Hessian” – joint work with Aleksandr Shestakov, Nail Bashirov, Alexander Gasnikov, Martin Takáč, Aleksandr Beznosikov and Dmitry Kamzolov.

“Apertus: Democratizing Open and Compliant LLMs for Global Language Environments” – a project by Team Apertus within the Swiss AI Initiative.

“Benchmarking Optimizers for Large Language Model Pretraining” – joint work with Matteo Pagliardini and Martin Jaggi.

“Gradient-Normalized Smoothness for Optimization with Approximate Hessians” – joint work with Martin Jaggi and Nikita Doikov.

“Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed” (with Savelii Chezhegov, Yaroslav Klyukin, Aleksandr Beznosikov, Alexander Gasnikov, Samuel Horváth, Martin Takáč and Eduard Gorbunov).

Between 15 February and 15 April, I will work as a Visiting Student at the Machine Learning Department of MBZUAI under the supervision of Professor Martin Takáč.

“Sign Operator for Coping with Heavy-Tailed Noise: High Probability Convergence Bounds with Extensions to Distributed Optimization and Comparison Oracle” – joint work with Nikita Kornilov, Philip Zmushko, Alexander Gasnikov and Aleksandr Beznosikov.

“Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning” – joint work with Philip Zmushko, Alexander Pichugin and Aleksandr Beznosikov.