Vyacheslav Kungurtsev

104 papers A* 7A 3B 1Misc 3Journal 82Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management. CoRR Daniel Mastropietro, Vyacheslav Kungurtsev
2026	J	`jnl`	Noncooperative Human-AI Agent Dynamics. CoRR Dylan Waldner, Vyacheslav Kungurtsev, Mitchelle Ashimosi
2026	J	`jnl`	Numerical Algorithms for Partially Segregated Elliptic Systems. CoRR Farid Bozorgnia, Avetik Arakelyan, Vyacheslav Kungurtsev, Jan Valdman
2026	J	`jnl`	Probabilistic iterative hard thresholding for sparse learning. Comput. Optim. Appl. Matteo Bergamaschi, Andrea Cristofari, Vyacheslav Kungurtsev, Francesco Rinaldi
2026	J	`jnl`	The Stochastic Ravine Accelerated Gradient Method with General Extrapolation Coefficients. J. Optim. Theory Appl. Hédy Attouch, Jalal Fadili, Vyacheslav Kungurtsev
2026	J	`jnl`	Towards Tsallis Fully Probabilistic Design. CoRR Vyacheslav Kungurtsev, Giovanni Russo
2025	J	`jnl`	"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning". CoRR Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sír, Martin Krutsky
2025	J	`jnl`	A Stochastic-Gradient-Based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. SIAM J. Optim. Frank E. Curtis, Vyacheslav Kungurtsev, Daniel P. Robinson, Qi Wang
2025	J	`jnl`	A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes. CoRR Kemboi Cheruiyot, Nickson Kiprotich, Vyacheslav Kungurtsev, Kennedy Mugo, Vivian Mwirigi, Marvin Ngesa
2025	A	`conf`	Binarizing Physics-Inspired GNNs for Combinatorial Optimization. ECAI Martin Krutsky, Gustav Sír, Vyacheslav Kungurtsev, Georgios Korpas
2025	J	`jnl`	Binarizing Physics-Inspired GNNs for Combinatorial Optimization. CoRR Martin Krutsky, Gustav Sír, Vyacheslav Kungurtsev, Georgios Korpas
2025	J	`jnl`	CONCORD: Concept-Informed Diffusion for Dataset Distillation. CoRR Jianyang Gu, Haonan Wang, Ruoxi Jia, Saeed Vahidian, Vyacheslav Kungurtsev, Wei Jiang, Yiran Chen
2025	J	`jnl`	Federated Sinkhorn. CoRR Jeremy Kulcsar, Vyacheslav Kungurtsev, Georgios Korpas, Giulio Giaconi, William Shoosmith
2025	J	`jnl`	Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size. CoRR Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov, Mohsen Yousefnezhad
2025	A*	`conf`	Group Distributionally Robust Dataset Distillation with Risk Minimization. ICLR Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang, Yiran Chen
2025	J	`jnl`	Leveraging Gradient Noise for Detection and Filtering of Byzantine Clients. IEEE Access Latifa Errami, Vyacheslav Kungurtsev, El Houcine Bergou
2025	J	`jnl`	Machine Learning Algorithms for Improving Black Box Optimization Solvers. CoRR Morteza Kimiaei, Vyacheslav Kungurtsev
2025	J	`jnl`	Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization. CoRR Morteza Kimiaei, Vyacheslav Kungurtsev, Brian Olimba
2025	J	`jnl`	Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations. CoRR Vyacheslav Kungurtsev, Gustav Sir, Akhil S. Anand, Sebastien Gros, Haozhe Tian, Homayoun Hamedmoghadam
2025	J	`jnl`	Parametric Semidefinite Programming: Geometry of the Trajectory of Solutions. Math. Oper. Res. Antonio Bellon, Didier Henrion, Vyacheslav Kungurtsev, Jakub Marecek
2024	J	`jnl`	Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning. CoRR Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu, Saeed Vahidian, Anthony Quinn, Fadwa Idlahcen, Yiran Chen
2024	A*	`conf`	Efficient Dataset Distillation via Minimax Diffusion. CVPR Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen
2024	J	`jnl`	Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference. CoRR Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi, Bapi Chatterjee, Jakub Marecek
2024	A	`conf`	Federated SGD with Local Asynchrony. ICDCS Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
2024	J	`jnl`	Group Distributionally Robust Dataset Distillation with Risk Minimization. CoRR Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang, Yiran Chen
2024	J	`jnl`	Inexact direct-search methods for bilevel optimization problems. Comput. Optim. Appl. Youssef Diouane, Vyacheslav Kungurtsev, Francesco Rinaldi, Damiano Zeffiro
2024	J	`jnl`	Iteration Complexity of Variational Quantum Algorithms. Quantum Vyacheslav Kungurtsev, Georgios Korpas, Jakub Marecek, Elton Yechao Zhu
2024	J	`jnl`	Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons. CoRR Vyacheslav Kungurtsev, Petr Rysavý, Fadwa Idlahcen, Pavel Rytír, Ales Wodecki
2024	J	`jnl`	Probabilistic Iterative Hard Thresholding for Sparse Learning. CoRR Matteo Bergamaschi, Andrea Cristofari, Vyacheslav Kungurtsev, Francesco Rinaldi
2024	J	`jnl`	Retraction-Based Direct Search Methods for Derivative Free Riemannian Optimization. J. Optim. Theory Appl. Vyacheslav Kungurtsev, Francesco Rinaldi, Damiano Zeffiro
2024	J	`jnl`	The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets. CoRR Erwann de Belloy de Saint-Lienard, Jakub Marecek, Vyacheslav Kungurtsev
2024	J	`jnl`	Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. SIAM J. Optim. Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek, André Uschmajew
2024	A*	`conf`	Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. NeurIPS Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang, Chen Chen, Bill Lin
2024	J	`jnl`	Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. CoRR Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang, Chen Chen, Bill Lin
2024	J	`jnl`	Trilevel and multilevel optimization using monotone operator theory. Math. Methods Oper. Res. Allahkaram Shafiei, Vyacheslav Kungurtsev, Jakub Marecek
2024	—	`conf`	Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. ECCV (78) Yuqi Jia, Saeed Vahidian, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, Yiran Chen
2023	J	`jnl`	A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. CoRR Frank E. Curtis, Vyacheslav Kungurtsev, Daniel P. Robinson, Qi Wang
2023	J	`jnl`	A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond. CoRR Philip Intallura, Georgios Korpas, Sudeepto Chakraborty, Vyacheslav Kungurtsev, Jakub Marecek
2023	J	`jnl`	Decentralized Asynchronous Nonconvex Stochastic Optimization on Directed Graphs. IEEE Trans. Control. Netw. Syst. Vyacheslav Kungurtsev, Mahdi Morafah, Tara Javidi, Gesualdo Scutari
2023	J	`jnl`	Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. Mach. Learn. Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
2023	J	`jnl`	Efficient Dataset Distillation via Minimax Diffusion. CoRR Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen
2023	A*	`conf`	Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. AAAI Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
2023	J	`jnl`	Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. Trans. Mach. Learn. Res. Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli
2023	J	`jnl`	On the ergodic control of ensembles in the presence of non-linear filters. Autom. Vyacheslav Kungurtsev, Jakub Marecek, Ramen Ghosh, Robert Shorten
2023	J	`jnl`	Quantum Solutions to the Privacy vs. Utility Tradeoff. CoRR Sagnik Chatterjee, Vyacheslav Kungurtsev
2023	J	`jnl`	Regularized quasi-monotone method for stochastic optimization. Optim. Lett. Vyacheslav Kungurtsev, Vladimir Shikhman
2023	J	`jnl`	Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions. CoRR Johannes Aspman, Vyacheslav Kungurtsev, Reza Roohi Seraji
2023	J	`jnl`	Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation. CoRR Ales Wodecki, Pavel Rytír, Vyacheslav Kungurtsev, Jakub Marecek
2023	J	`jnl`	Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. CoRR Yuqi Jia, Saeed Vahidian, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, Yiran Chen
2023	A*	`conf`	When Do Curricula Work in Federated Learning? ICCV Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
2022	—	`conf`	A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization. CDC Dinesh Krishnamoorthy, Vyacheslav Kungurtsev
2022	J	`jnl`	A Stochastic Levenberg-Marquardt Method Using Random Models with Complexity Results. SIAM/ASA J. Uncertain. Quantification El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer
2022	J	`jnl`	A Subsampling Line-Search Method with Second-Order Results. INFORMS J. Optim. El Houcine Bergou, Youssef Diouane, Vladimir Kunc, Vyacheslav Kungurtsev, Clément W. Royer
2022	J	`jnl`	Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. Optim. Methods Softw. Francisco Facchinei, Vyacheslav Kungurtsev, Lorenzo Lampariello, Gesualdo Scutari
2022	J	`jnl`	Distributed stochastic nonsmooth nonconvex optimization. Oper. Res. Lett. Vyacheslav Kungurtsev
2022	J	`jnl`	Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces. CoRR Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
2022	J	`jnl`	Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. J. Mach. Learn. Res. Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli
2022	J	`jnl`	Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. CoRR Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli
2022	J	`jnl`	Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD. CoRR Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
2022	J	`jnl`	Stochastic Langevin Differential Inclusions with Applications to Machine Learning. CoRR Fabio V. Difonzo, Vyacheslav Kungurtsev, Jakub Marecek
2022	J	`jnl`	Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. CoRR Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek, André Uschmajew
2022	J	`jnl`	When Do Curricula Work in Federated Learning? CoRR Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
2021	J	`jnl`	A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Least-Squares Problems. SIAM J. Sci. Comput. El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer
2021	J	`jnl`	A zeroth order method for stochastic weakly convex optimization. Comput. Optim. Appl. Vyacheslav Kungurtsev, Francesco Rinaldi
2021	A*	`conf`	Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees. AAAI Vyacheslav Kungurtsev, Malcolm Egan, Bapi Chatterjee, Dan Alistarh
2021	J	`jnl`	Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions. IEEE Trans. Autom. Control. Loris Cannelli, Francisco Facchinei, Gesualdo Scutari, Vyacheslav Kungurtsev
2021	J	`jnl`	Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure. Optim. Lett. El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev
2021	J	`jnl`	Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo. CoRR Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
2021	J	`jnl`	Decentralized Langevin Dynamics over a Directed Graph. CoRR Alexander Kolesov, Vyacheslav Kungurtsev
2021	A*	`conf`	Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent. AAAI Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
2021	J	`jnl`	Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity. Math. Oper. Res. Francisco Facchinei, Vyacheslav Kungurtsev, Lorenzo Lampariello, Gesualdo Scutari
2021	J	`jnl`	Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. CoRR Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli
2021	J	`jnl`	Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice. CoRR Alberto Schiabel, Vyacheslav Kungurtsev, Jakub Marecek
2021	J	`jnl`	Trilevel and Multilevel Optimization using Monotone Operator Theory. CoRR Allahkaram Shafiei, Vyacheslav Kungurtsev, Jakub Marecek
2020	J	`jnl`	A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Inverse Problems. CoRR El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer
2020	J	`jnl`	A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. SIAM J. Optim. Philip E. Gill, Vyacheslav Kungurtsev, Daniel P. Robinson
2020	—	`conf`	A Two-Step Pre-Processing for Semidefinite Programming. CDC Vyacheslav Kungurtsev, Jakub Marecek
2020	J	`jnl`	Asynchronous parallel algorithms for nonconvex optimization. Math. Program. Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2020	J	`jnl`	Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems. J. Optim. Theory Appl. El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev
2020	J	`jnl`	Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent. CoRR Dan Alistarh, Bapi Chatterjee, Vyacheslav Kungurtsev
2020	—	`conf`	Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang
2020	J	`jnl`	Reinforcement Learning Based on Real-Time Iteration NMPC. CoRR Mario Zanon, Vyacheslav Kungurtsev, Sébastien Gros
2020	J	`jnl`	Second-Order Guarantees of Distributed Gradient Algorithms. SIAM J. Optim. Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
2020	J	`jnl`	Stochastic Gradient Langevin Dynamics on a Distributed Network. CoRR Vyacheslav Kungurtsev
2020	J	`jnl`	Stochastic Gradient Langevin with Delayed Gradients. CoRR Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh
2019	A	`conf`	Lifted Weight Learning of Markov Logic Networks Revisited. AISTATS Ondrej Kuzelka, Vyacheslav Kungurtsev
2019	J	`jnl`	Lifted Weight Learning of Markov Logic Networks Revisited. CoRR Ondrej Kuzelka, Vyacheslav Kungurtsev
2018	J	`jnl`	Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems. CoRR Vyacheslav Kungurtsev, Tomás Pevný
2018	J	`jnl`	Algorithms for solving optimization problems arising from deep neural net models: smooth problems. CoRR Vyacheslav Kungurtsev, Tomás Pevný
2018	J	`jnl`	Second-order Guarantees of Distributed Gradient Algorithms. CoRR Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
2018	—	`conf`	Second-order Guarantees of Gradient Algorithms over Networks. Allerton Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
2017	J	`jnl`	A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems. SIAM J. Optim. Vyacheslav Kungurtsev, Johannes Jäschke
2017	J	`jnl`	A stabilized SQP method: superlinear convergence. Math. Program. Philip E. Gill, Vyacheslav Kungurtsev, Daniel P. Robinson
2017	Misc	`conf`	Asynchronous parallel nonconvex large-scale optimization. ICASSP Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2017	B	`conf`	Capacity sensitivity in additive non-Gaussian noise channels. ISIT Malcolm Egan, Samir Medina Perlaza, Vyacheslav Kungurtsev
2017	—	`conf`	Essentially cyclic asynchronous nonconvex large-scale optimization. SPAWC Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2016	J	`jnl`	Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence. CoRR Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2016	Misc	`conf`	Parallel asynchronous lock-free algorithms for nonconvex big-data optimization. ACSSC Loris Cannelli, Gesualdo Scutari, Francisco Facchinei, Vyacheslav Kungurtsev
2015	J	`jnl`	Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization. IEEE Trans. Signal Process. Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2014	Misc	`conf`	Flexible selective parallel algorithms for big data optimization. ACSSC Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2014	J	`jnl`	Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization. CoRR Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
2014	—	`conf`	Linear convergence of distributed multiple shooting. ECC Vyacheslav Kungurtsev, Attila Kozma, Moritz Diehl
2014	J	`jnl`	Sequential quadratic programming methods for parametric nonlinear optimization. Comput. Optim. Appl. Vyacheslav Kungurtsev, Moritz Diehl
2013	—	`—`	Second-derivative sequential quadratic programming methods for nonlinear optimization Vyacheslav Kungurtsev