102 citations · 215 across the 26 of their papers we have counts for
30 papers
Importance-Sampling Estimation of Gaussian Molecular Shape Overlap: Exact Union Volumes and Confidence-Bounded Virtual Screening
Egor I. Tuzharov, Alexandra V. Bochenkova, Yury Maximov
Gaussian descriptions of molecular shape underpin 3D shape-based virtual screening, but existing methods evaluate Gaussian overlap analytically. The widely used first-order approxi…
Long-term drought prediction using deep neural networks based on geospatial weather data
Alexander Marusov, Vsevolod Grabar, Yury Maximov +3
The problem of high-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. Yet, it is still unsolved with reasonable accuracy due t…
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich +4
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power gr…
Long-term hail risk assessment with deep neural networks
Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov +1
Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and,…
Ranking-Based Physics-Informed Line Failure Detection in Power Grids
Aleksandra Burashnikova, Wenting Li, Massih Amini +2
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…
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev +3
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncert…
Data-Driven Stochastic AC-OPF using Gaussian Processes
Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev +4
In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into…
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
Aleksandra Burashnikova, Yury Maximov, Marianne Clausel +3
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 Reco…
Self-Training: A Survey
Massih-Reza Amini, Vasilii Feofanov, Loic Pauletto +3
Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations. Because this framework is relevant…
Recommender systems: when memory matters
Aleksandra Burashnikova, Marianne Clausel, Massih-Reza Amini +2
In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback. We propose an online algorithm, where m…
Importance sampling approach to chance-constrained DC optimal power flow
Aleksander Lukashevich, Vyacheslav Gorchakov, Petr Vorobev +2
Despite significant economic and ecological effects, a higher level of renewable energy generation leads to increased uncertainty and variability in power injections, thus compromi…
Power Grid Reliability Estimation via Adaptive Importance Sampling
Aleksandr Lukashevich, Yury Maximov
Electricity production currently generates approximately 25% of greenhouse gas emissions in the USA. Thus, increasing the amount of renewable energy is a key step to carbon neutral…
Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation
Aleksandra Burashnikova, Marianne Clausel, Charlotte Laclau +3
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. The pr…
A Bayesian Framework for Power System Components Identification
Artem Mikhalev, Alexander Emchinov, Samuel Chevalier +2
Having actual models for power system components (such as generators and loads or auxiliary equipment) is vital to correctly assess the power system operating state and to establis…
Tractable Minor-free Generalization of Planar Zero-field Ising Models
Valerii Likhosherstov, Yury Maximov, Michael Chertkov
We present a new family of zero-field Ising models over binary variables/spins obtained by consecutive "gluing" of planar and -sized components and subsets of at most thr…
A New Family of Tractable Ising Models
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 -sized components along with subsets of at mos…
Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
Alexandra Burashnikova, Yury Maximov, Massih-Reza Amini
In this paper, we propose a robust sequential learning strategy for training large-scale Recommender Systems (RS) over implicit feedback mainly in the form of clicks. Our approach…
Inference and Sampling of -free Ising Models
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. The tractability also implies an ability to…
Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
Michael Chertkov, Vladimir Chernyak, Yury Maximov
Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main infer…
Entropy Penalized Semidefinite Programming
Mikhail Krechetov, Jakub Marecek, Yury Maximov +1
Low-rank methods for semidefinite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-b…
Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Andrii Riazanov, Yury Maximov, Michael Chertkov
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model,…
Importance sampling the union of rare events with an application to power systems analysis
Art B. Owen, Yury Maximov, Michael Chertkov
We consider importance sampling to estimate the probability of a union of rare events defined by a random variable . The sampler we study has been use…
Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization
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. The method improves an existing…
Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
Sumit Sidana, Mikhail Trofimov, Oleg Horodnitskii +3
In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss.…