Tight Risk Bounds for Multi-Class Margin Classifiers. - Yury Maximov; Daria Reshetova (CoRR, 2015)

Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm. - Yury Maximov; Massih-Reza Amini; Zaïd Harchaoui (CoRR, 2016)

Efficient rank minimization to tighten semidefinite programming for unconstrained binary quadratic optimization. - Roman Pogodin; Mikhail Krechetov; Yury Maximov (Allerton, 2017)

Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification. - Bikash Joshi; Massih-Reza Amini; Ioannis Partalas; Franck Iutzeler; Yury Maximov (NIPS, 2017)

Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems. - Mikhail Trofimov; Sumit Sidana; Oleh Horodnitskii; Charlotte Laclau; Yury Maximov; Massih-Reza Amini (CoRR, 2017)

Importance sampling the union of rare events with an application to power systems analysis. - Art B. Owen; Yury Maximov; Michael Chertkov (CoRR, 2017)

Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow. - Andrii Riazanov; Yury Maximov; Michael Chertkov (ACC, 2018)

Heterogeneous Dyadic Multi-task Learning with Implicit Feedback. - Simon Moura; Amir Asarbaev; Massih-Reza Amini; Yury Maximov (ICONIP (3), 2018)

Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm (Extended Abstract). - Yury Maximov; Massih-Reza Amini; Zaïd Harchaoui (IJCAI, 2018)

Gauges, Loops, and Polynomials for Partition Functions of Graphical Models. - Michael Chertkov; Yury Maximov (CoRR, 2018)

Inference and Sampling of K33-free Ising Models. - Valerii Likhosherstov; Yury Maximov; Michael Chertkov (CoRR, 2018)

Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm. - Yury Maximov; Massih-Reza Amini; Zaïd Harchaoui (J. Artif. Intell. Res., 2018)

Inference and Sampling of $K_33$-free Ising Models. - Valerii Likhosherstov; Yury Maximov; Misha Chertkov (ICML, 2019)

Entropy-Penalized Semidefinite Programming. - Mikhail Krechetov; Jakub Marecek; Yury Maximov; Martin Takác 001 (IJCAI, 2019)

Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems. - Aleksandra Burashnikova; Yury Maximov; Massih-Reza Amini (ECML/PKDD (3), 2019)

Learning a Generator Model from Terminal Bus Data. - Nikolay Stulov; Dejan J. Sobajic; Yury Maximov; Deepjyoti Deka; Michael Chertkov (CoRR, 2019)

A New Family of Tractable Ising Models. - Valerii Likhosherstov; Yury Maximov; Michael Chertkov (CoRR, 2019)

Tractable Minor-free Generalization of Planar Zero-field Ising Models. - Valerii Likhosherstov; Yury Maximov; Michael Chertkov (CoRR, 2019)

Learning over no-Preferred and Preferred Sequence of items for Robust Recommendation. - Aleksandra Burashnikova; Marianne Clausel; Charlotte Laclau; Frack Iutzeller; Yury Maximov; Massih-Reza Amini (CoRR, 2020)

User preference and embedding learning with implicit feedback for recommender systems. - Sumit Sidana; Mikhail Trofimov; Oleh Horodnytskyi; Charlotte Laclau; Yury Maximov; Massih-Reza Amini (Data Min. Knowl. Discov., 2021)

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation. - Aleksandra Burashnikova; Yury Maximov; Marianne Clausel; Charlotte Laclau; Franck Iutzeler; Massih-Reza Amini (J. Artif. Intell. Res., 2021)

Recommender Systems: When Memory Matters. - Aleksandra Burashnikova; Marianne Clausel; Massih-Reza Amini; Yury Maximov; Nicolas Dante (ECIR (2), 2022)

Self-Training: A Survey. - Massih-Reza Amini; Vasilii Feofanov; Loïc Pauletto; Emilie Devijver; Yury Maximov (CoRR, 2022)

Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract). - Aleksandra Burashnikova; Yury Maximov; Marianne Clausel; Charlotte Laclau; Franck Iutzeler; Massih-Reza Amini (CoRR, 2022)

Data-Driven Stochastic AC-OPF using Gaussian Processes. - Mile Mitrovic; Aleksandr Lukashevich; Petr Vorobev; Vladimir V. Terzija; Semen Budenny; Yury Maximov; Deepjyoti Deka (CoRR, 2022)

Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes. - Mile Mitrovic; Aleksandr Lukashevich; Petr Vorobev; Vladimir V. Terzija; Yury Maximov; Deepjyoti Deka (CoRR, 2022)

Predicting spatial distribution of Palmer Drought Severity Index. - Vsevolod Grabar; Yury Maximov; Alexey Zaytsev 002 (CoRR, 2022)

Ranking-Based Physics-Informed Line Failure Detection in Power Grids. - Aleksandra Burashnikova; Wenting Li; Massih Amini; Deepjyoti Deka; Yury Maximov (CoRR, 2022)

Long-term hail risk assessment with deep neural networks. - Ivan Lukyanenko; Mikhail Mozikov; Yury Maximov; Ilya Makarov (CoRR, 2022)

Power Grid Reliability Estimation via Adaptive Importance Sampling. - Aleksandr Lukashevich; Yury Maximov (IEEE Control. Syst. Lett., 2022)

Efficient numerical methods to solve sparse linear equations with application to PageRank. - Anton Anikin 002; Alexander V. Gasnikov; Alexander Gornov; Dmitry Kamzolov; Yury Maximov; Yurii E. Nesterov (Optim. Methods Softw., 2022)

Long-Term Hail Risk Assessment with Deep Neural Networks. - Mikhail Mozikov; Ivan Lukyanenko; Ilya Makarov; Alexander Bulkin; Yury Maximov (IWANN (1), 2023)

GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow. - Mile Mitrovic; Ognjen Kundacina; Aleksandr Lukashevich; Petr Vorobev; Vladimir V. Terzija; Yury Maximov; Deepjyoti Deka (CoRR, 2023)

Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study. - Valeriy Shevchenko; Daria Taniushkina; Aleksandr Lukashevich; Aleksander Bulkin; Roland Grinis; Kirill Kovalev; Veronika Narozhnaia; Nazar Sotiriadi; Alexander Krenke; Yury Maximov (CoRR, 2023)

CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics. - Vsevolod Morozov; Artem Galliamov; Aleksandr Lukashevich; Antonina Kurdukova; Yury Maximov (CoRR, 2023)

GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow. - Mile Mitrovic; Ognjen Kundacina; Aleksandr Lukashevich; Semen A. Budennyy; Petr Vorobev; Vladimir V. Terzija; Yury Maximov; Deepjyoti Deka (Softw. Impacts, 2023)

From Data to Decisions: Streamlining Geospatial Operations with Multimodal GlobeFlowGPT. - Danil Kononykhin; Mikhail Mozikov; Kirill Mishtal; Pavel Kuznetsov; Dmitrii Abramov; Nazar Sotiriadi; Yury Maximov; Andrey V. Savchenko; Ilya Makarov (SIGSPATIAL/GIS, 2024)

Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study. - Valeriy Shevchenko; Aleksandr Lukashevich; Daria Taniushkina; Alexander Bulkin; Roland Grinis; Kirill Kovalev; Veronika Narozhnaia; Nazar Sotiriadi; Alexander Krenke; Yury Maximov (IEEE Access, 2024)

Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks. - Joe Gorka; Tim Hsu; Wenting Li; Yury Maximov; Line Roald (CoRR, 2024)

A-Priori Reduction of Scenario Approximation for Automated Generation Control in High-Voltage Power Grids With Renewable Energy. - Aleksandr Lukashevich; Aleksandr Bulkin; Yury Maximov (IEEE Control. Syst. Lett., 2024)

Long-term drought prediction using deep neural networks based on geospatial weather data. - Alexander Marusov; Vsevolod Grabar; Yury Maximov; Nazar Sotiriadi; Alexander Bulkin; Alexey Zaytsev 002 (Environ. Model. Softw., 2024)

Importance Sampling Approach to Chance-Constrained DC Optimal Power Flow. - Aleksander Lukashevich; Vyacheslav Gorchakov; Petr Vorobev; Deepjyoti Deka; Yury Maximov (IEEE Trans. Control. Netw. Syst., 2024)

A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment. - Polina Pilyugina; Timofey Chernikov; Maria Smirnova; Alexey Zaytsev 002; Alexander Bulkin; Evgeny Burnaev; Ilya S. Belalov; Nazar Sotiriadi; Albert Efimov; Yury Maximov; Oleg Anisimov (IEEE Access, 2025)

Self-training: A survey. - Massih-Reza Amini; Vasilii Feofanov; Loïc Pauletto; Lies Hadjadj; Emilie Devijver; Yury Maximov (Neurocomputing, 2025)