no code implementations • 22 Mar 2024 • Joe Gorka, Tim Hsu, Wenting Li, Yury Maximov, Line Roald
Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures.
1 code implementation • 24 Oct 2023 • Valeriy Shevchenko, Daria Taniushkina, Aleksander Lukashevich, Aleksandr Bulkin, Roland Grinis, Kirill Kovalev, Veronika Narozhnaia, Nazar Sotiriadi, Alexander Krenke, Yury Maximov
This study represents a pioneering effort in utilizing machine learning methods to assess the impact of climate change on agricultural land suitability under various carbon emissions scenarios.
no code implementations • 24 Oct 2023 • Vsevolod Morozov, Artem Galliamov, Aleksandr Lukashevich, Antonina Kurdukova, Yury Maximov
Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters.
no code implementations • 12 Sep 2023 • Vsevolod Grabar, Alexander Marusov, Yury Maximov, Nazar Sotiriadi, Alexander Bulkin, Alexey Zaytsev
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance.
no code implementations • 16 Feb 2023 • Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy.
no code implementations • 31 Aug 2022 • Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov, Ilya Makarov
But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area.
no code implementations • 31 Aug 2022 • Aleksandra Burashnikova, Wenting Li, Massih Amini, Deepjoyti Deka, Yury Maximov
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment failures.
1 code implementation • 30 Aug 2022 • Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty.
1 code implementation • 21 Jul 2022 • Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Semen Budenny, Yury Maximov, Deepjoyti Deka
Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies.
1 code implementation • 26 Feb 2022 • Aleksandra Burashnikova, Yury Maximov, Marianne Clausel, Charlotte Laclau, Franck Iutzeler, Massih-Reza Amini
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012. 06910], where we proposed a theoretically supported sequential strategy for training a large-scale Recommender System (RS) over implicit feedback, mainly in the form of clicks.
no code implementations • 24 Feb 2022 • Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto, Lies Hadjadj, Emilie Devijver, Yury Maximov
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations.
no code implementations • 4 Dec 2021 • Aleksandra Burashnikova, Marianne Clausel, Massih-Reza Amini, Yury Maximov, Nicolas Dante
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback.
1 code implementation • 12 Dec 2020 • Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau, Frack Iutzeller, Yury Maximov, Massih-Reza Amini
In this paper, we propose a theoretically founded sequential strategy for training large-scale Recommender Systems (RS) over implicit feedback, mainly in the form of clicks.
no code implementations • 22 Oct 2019 • Valerii Likhosherstov, Yury Maximov, Michael Chertkov
We present a new family of zero-field Ising models over $N$ binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components and subsets of at most three vertices into a tree.
no code implementations • 14 Jun 2019 • Valerii Likhosherstov, Yury Maximov, Michael Chertkov
To illustrate the utility of the new family of tractable graphical models, we first build an $O(N^{3/2})$ algorithm for inference and sampling of the K5-minor-free zero-field Ising models - an extension of the planar zero-field Ising models - which is neither genus- nor treewidth-bounded.
Data Structures and Algorithms Statistical Mechanics Data Analysis, Statistics and Probability Computation
no code implementations • 21 Feb 2019 • Alexandra Burashnikova, Yury Maximov, Massih-Reza Amini
This is to prevent from an abnormal number of clicks over some targeted items, mainly due to bots; or very few user interactions.
no code implementations • 3 Jan 2019 • Nikolay Stulov, Dejan J Sobajic, Yury Maximov, Deepjyoti Deka, Michael Chertkov
In this work we investigate approaches to reconstruct generator models from measurements available at the generator terminal bus using machine learning (ML) techniques.
2 code implementations • 22 Dec 2018 • Valerii Likhosherstov, Yury Maximov, Michael Chertkov
We call an Ising model tractable when it is possible to compute its partition function value (statistical inference) in polynomial time.
no code implementations • 12 Nov 2018 • Michael Chertkov, Vladimir Chernyak, Yury Maximov
We show that the Gauge Function has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph.
no code implementations • 20 Oct 2017 • Andrii Riazanov, Yury Maximov, Michael Chertkov
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems.
1 code implementation • 5 Aug 2017 • Roman Pogodin, Mikhail Krechetov, Yury Maximov
We propose a method for low-rank semidefinite programming in application to the semidefinite relaxation of unconstrained binary quadratic problems.
Optimization and Control
1 code implementation • 29 Apr 2017 • Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii, Charlotte Laclau, Yury Maximov, Massih-Reza Amini
The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users' preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering.
1 code implementation • NeurIPS 2017 • Bikash Joshi, Massih-Reza Amini, Ioannis Partalas, Franck Iutzeler, Yury Maximov
We address the problem of multi-class classification in the case where the number of classes is very large.
no code implementations • 2 Jul 2016 • Yury Maximov, Massih-Reza Amini, Zaid Harchaoui
We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model.
no code implementations • 10 Jul 2015 • Yury Maximov, Daria Reshetova
We consider a problem of risk estimation for large-margin multi-class classifiers.