1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
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…
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…
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…