Vyacheslav Kungurtsev

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 70

Top Topics

Optimization Problems

Randomized Algorithms

Markov Logic Networks

Top Venues

Comput. Optim. Appl.

Publications

Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek, André Uschmajew
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. SIAM J. Optim. 34 (1) (2024)
Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang, Yiran Chen
Group Distributionally Robust Dataset Distillation with Risk Minimization. CoRR (2024)
Erwann de Belloy de Saint-Lienard, Jakub Marecek, Vyacheslav Kungurtsev
The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets. CoRR (2024)
Allahkaram Shafiei, Vyacheslav Kungurtsev, Jakub Marecek
Trilevel and multilevel optimization using monotone operator theory. Math. Methods Oper. Res. 99 (1) (2024)
Vyacheslav Kungurtsev, Petr Rysavy, Fadwa Idlahcen, Pavel Rytír, Ales Wodecki
Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons. CoRR (2024)
Youssef Diouane, Vyacheslav Kungurtsev, Francesco Rinaldi, Damiano Zeffiro
Inexact direct-search methods for bilevel optimization problems. Comput. Optim. Appl. 88 (2) (2024)
Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi, Bapi Chatterjee, Jakub Marecek
Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference. CoRR (2024)
Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. AAAI (2023)
Philip Intallura, Georgios Korpas, Sudeepto Chakraborty, Vyacheslav Kungurtsev, Jakub Marecek
A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond. CoRR (2023)
Ales Wodecki, Pavel Rytír, Vyacheslav Kungurtsev, Jakub Marecek
Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation. CoRR (2023)
Yuqi Jia, Saeed Vahidian, Jingwei Sun, Jianyi Zhang, Vyacheslav Kungurtsev, Neil Zhenqiang Gong, Yiran Chen
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. CoRR (2023)
Vyacheslav Kungurtsev, Mahdi Morafah, Tara Javidi, Gesualdo Scutari
Decentralized Asynchronous Nonconvex Stochastic Optimization on Directed Graphs. IEEE Trans. Control. Netw. Syst. 10 (4) (2023)
Sagnik Chatterjee, Vyacheslav Kungurtsev
Quantum Solutions to the Privacy vs. Utility Tradeoff. CoRR (2023)
Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. Mach. Learn. 112 (8) (2023)
Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen
Efficient Dataset Distillation via Minimax Diffusion. CoRR (2023)
Johannes Aspman, Vyacheslav Kungurtsev, Reza Roohi Seraji
Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions. CoRR (2023)
Frank E. Curtis, Vyacheslav Kungurtsev, Daniel P. Robinson, Qi Wang
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. CoRR (2023)
Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
When Do Curricula Work in Federated Learning? ICCV (2023)
Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. Trans. Mach. Learn. Res. 2023 (2023)
Vyacheslav Kungurtsev, V. Shikhman
Regularized quasi-monotone method for stochastic optimization. Optim. Lett. 17 (5) (2023)
Vyacheslav Kungurtsev, Jakub Marecek, Ramen Ghosh, Robert Shorten
On the ergodic control of ensembles in the presence of non-linear filters. Autom. 152 (2023)
El Houcine Bergou, Youssef Diouane, Vladimir Kunc, Vyacheslav Kungurtsev, Clément W. Royer
A Subsampling Line-Search Method with Second-Order Results. INFORMS J. Optim. 4 (4) (2022)
Dinesh Krishnamoorthy, Vyacheslav Kungurtsev
A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization. CDC (2022)
Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek, André Uschmajew
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. CoRR (2022)
Fabio V. Difonzo, Vyacheslav Kungurtsev, Jakub Marecek
Stochastic Langevin Differential Inclusions with Applications to Machine Learning. CoRR (2022)
Vyacheslav Kungurtsev
Distributed stochastic nonsmooth nonconvex optimization. Oper. Res. Lett. 50 (6) (2022)
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer
A Stochastic Levenberg-Marquardt Method Using Random Models with Complexity Results. SIAM/ASA J. Uncertain. Quantification 10 (1) (2022)
Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. J. Mach. Learn. Res. 23 (2022)
Francisco Facchinei, Vyacheslav Kungurtsev, Lorenzo Lampariello, Gesualdo Scutari
Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. Optim. Methods Softw. 37 (4) (2022)
Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces. CoRR (2022)
Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin
When Do Curricula Work in Federated Learning? CoRR (2022)
Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD. CoRR (2022)
Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. CoRR (2022)
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev
Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure. Optim. Lett. 15 (4) (2021)
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer
A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Least-Squares Problems. SIAM J. Sci. Comput. 43 (5) (2021)
Loris Cannelli, Francisco Facchinei, Gesualdo Scutari, Vyacheslav Kungurtsev
Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions. IEEE Trans. Autom. Control. 66 (10) (2021)
Francisco Facchinei, Vyacheslav Kungurtsev, Lorenzo Lampariello, Gesualdo Scutari
Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity. Math. Oper. Res. 46 (2) (2021)
Alberto Schiabel, Vyacheslav Kungurtsev, Jakub Marecek
Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice. CoRR (2021)
Alexander Kolesov, Vyacheslav Kungurtsev
Decentralized Langevin Dynamics over a Directed Graph. CoRR (2021)
Vyacheslav Kungurtsev, Malcolm Egan, Bapi Chatterjee, Dan Alistarh
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees. AAAI (2021)
Vyacheslav Kungurtsev, Francesco Rinaldi
A zeroth order method for stochastic weakly convex optimization. Comput. Optim. Appl. 80 (3) (2021)
Allahkaram Shafiei, Vyacheslav Kungurtsev, Jakub Marecek
Trilevel and Multilevel Optimization using Monotone Operator Theory. CoRR (2021)
Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. CoRR (2021)
Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent. AAAI (2021)
Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo. CoRR (2021)
Dan Alistarh, Bapi Chatterjee, Vyacheslav Kungurtsev
Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent. CoRR (2020)
Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
Second-Order Guarantees of Distributed Gradient Algorithms. SIAM J. Optim. 30 (4) (2020)
Vyacheslav Kungurtsev
Stochastic Gradient Langevin Dynamics on a Distributed Network. CoRR (2020)
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev
Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems. J. Optim. Theory Appl. 185 (3) (2020)
Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh
Stochastic Gradient Langevin with Delayed Gradients. CoRR (2020)
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Asynchronous parallel algorithms for nonconvex optimization. Math. Program. 184 (1) (2020)
Mario Zanon, Vyacheslav Kungurtsev, Sébastien Gros
Reinforcement Learning Based on Real-Time Iteration NMPC. CoRR (2020)
Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang
Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM (2020)
Philip E. Gill, Vyacheslav Kungurtsev, Daniel P. Robinson
A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. SIAM J. Optim. 30 (2) (2020)
Vyacheslav Kungurtsev, Jakub Marecek
A Two-Step Pre-Processing for Semidefinite Programming. CDC (2020)
El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer
A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Inverse Problems. CoRR (2020)
Ondrej Kuzelka, Vyacheslav Kungurtsev
Lifted Weight Learning of Markov Logic Networks Revisited. AISTATS (2019)
Ondrej Kuzelka, Vyacheslav Kungurtsev
Lifted Weight Learning of Markov Logic Networks Revisited. CoRR (2019)
Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
Second-order Guarantees of Gradient Algorithms over Networks. Allerton (2018)
Vyacheslav Kungurtsev, Tomás Pevný
Algorithms for solving optimization problems arising from deep neural net models: smooth problems. CoRR (2018)
Vyacheslav Kungurtsev, Tomás Pevný
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems. CoRR (2018)
Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
Second-order Guarantees of Distributed Gradient Algorithms. CoRR (2018)
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Essentially cyclic asynchronous nonconvex large-scale optimization. SPAWC (2017)
Vyacheslav Kungurtsev, Johannes Jäschke
A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems. SIAM J. Optim. 27 (1) (2017)
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Asynchronous parallel nonconvex large-scale optimization. ICASSP (2017)
Malcolm Egan, Samir Medina Perlaza, Vyacheslav Kungurtsev
Capacity sensitivity in additive non-Gaussian noise channels. ISIT (2017)
Philip E. Gill, Vyacheslav Kungurtsev, Daniel P. Robinson
A stabilized SQP method: superlinear convergence. Math. Program. 163 (1-2) (2017)
Loris Cannelli, Gesualdo Scutari, Francisco Facchinei, Vyacheslav Kungurtsev
Parallel asynchronous lock-free algorithms for nonconvex big-data optimization. ACSSC (2016)
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence. CoRR (2016)
Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization. IEEE Trans. Signal Process. 63 (15) (2015)
Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization. CoRR (2014)
Vyacheslav Kungurtsev, Attila Kozma, Moritz Diehl
Linear convergence of distributed multiple shooting. ECC (2014)
Vyacheslav Kungurtsev, Moritz Diehl
Sequential quadratic programming methods for parametric nonlinear optimization. Comput. Optim. Appl. 59 (3) (2014)
Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari
Flexible selective parallel algorithms for big data optimization. ACSSC (2014)