Welcome!
I do research in Computer Science with a focus on Convex Optimization and Machine Learning.Now I am a PhD student at UCLouvain, Belgium. My scientific advisor is Yurii Nesterov. The topic of my thesis is second-order optimization algorithms.
Papers
- Optimization Methods for Fully Composite Problems, with Yurii Nesterov, 2021 (arXiv)
- Affine-invariant contracting-point methods for Convex Optimization, with Yurii Nesterov, 2020 (arXiv, code)
- Convex optimization based on global lower second-order models, with Yurii Nesterov, 2020 (NeurIPS proceedings, arXiv, code)
- Stochastic Subspace Cubic Newton Method, with Filip Hanzely, Peter Richtárik, and Yurii Nesterov, 2020 (ICML proceedings, arXiv)
- Inexact Tensor Methods with Dynamic Accuracies, with Yurii Nesterov, 2020 (ICML proceedings, arXiv, code)
- Contracting Proximal Methods for Smooth Convex Optimization, with Yurii Nesterov, 2019 (SIOPT journal, arXiv)
- Local convergence of tensor methods, with Yurii Nesterov, 2019 (Math. Program. journal, arXiv)
- Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method, with Yurii Nesterov, 2019 (JOTA journal, arXiv)
- Randomized Block Cubic Newton Method, with Peter Richtárik, 2018 (ICML proceedings, arXiv)
Talks
- March 4, 2021: Affine-invariant contracting-point methods for Convex Optimization, Symposium on Numerical Analysis and Optimization, UFPR, online (slides)
- October 28, 2020: Convex optimization based on global lower second-order models, NeurIPS, online (slides, poster)
- June 17, 2020: Inexact Tensor Methods with Dynamic Accuracies, ICML, online (slides, poster, video)
- October 8, 2019: Proximal Method with Contractions for Smooth Convex Optimization, ICTEAM seminar, Louvain-la-Neuve
- September 23, 2019: Proximal Method with Contractions for Smooth Convex Optimization,
Optimization and Learning for Data Science seminar
(invited by
Dmitry Grishchenko) Université Grenoble Alpes, Grenoble
(slides)
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- September 18, 2019: Complexity of Cubically Regularized Newton Method for Minimizing Uniformly Convex Functions, FGS-19, Nice (slides)
- August 5, 2019: Complexity of Cubically Regularized Newton Method for Minimizing Uniformly Convex Functions, ICCOPT, Berlin
- July 5, 2019: Randomized Block Cubic Newton Method,
Summer School on Optimization, Big Data and Applications, Veroli
[photo↓]
- June 28, 2019: Complexity of Cubically Regularized Newton Method for Minimizing Uniformly Convex Functions EUROPT, Glasgow
[photo↓]
- June 20, 2018: Randomized Block Cubic Newton Method, ICML, Stockholm
(slides,
poster,
video)
[photo↓]
- June 13, 2018: Randomized Block Cubic Newton Method,
X Traditional summer school on Optimization, Voronovo
[photo↓]
- April 20, 2018: Coordinate Optimization Methods for Machine Learning,
Seminar on Machine Learning, Voronovo
(slides)
[photo↓]
- October 31, 2017: Cubic regularization of Newton method, All Hands Meeting on Big Data Optimization (invited by Peter Richtárik), KAUST, Thuwal