Vyacheslav Kungurtsev

Orcid: 0000-0003-2229-8824

According to our database1, Vyacheslav Kungurtsev authored at least 62 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization.
SIAM J. Optim., March, 2024

Group Distributionally Robust Dataset Distillation with Risk Minimization.
CoRR, 2024

The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets.
CoRR, 2024

2023
Decentralized Asynchronous Nonconvex Stochastic Optimization on Directed Graphs.
IEEE Trans. Control. Netw. Syst., December, 2023

Regularized quasi-monotone method for stochastic optimization.
Optim. Lett., June, 2023

On the ergodic control of ensembles in the presence of non-linear filters.
Autom., June, 2023

Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence.
Trans. Mach. Learn. Res., 2023

Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo.
Mach. Learn., 2023

Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation.
CoRR, 2023

Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents.
CoRR, 2023

Efficient Dataset Distillation via Minimax Diffusion.
CoRR, 2023

Quantum Solutions to the Privacy vs. Utility Tradeoff.
CoRR, 2023

A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems.
CoRR, 2023

A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond.
CoRR, 2023

Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions.
CoRR, 2023

When Do Curricula Work in Federated Learning?
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
A Subsampling Line-Search Method with Second-Order Results.
INFORMS J. Optim., October, 2022

Distributed stochastic nonsmooth nonconvex optimization.
Oper. Res. Lett., 2022

Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case.
Optim. Methods Softw., 2022

A Stochastic Levenberg-Marquardt Method Using Random Models with Complexity Results.
SIAM/ASA J. Uncertain. Quantification, 2022

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks.
J. Mach. Learn. Res., 2022

Stochastic Langevin Differential Inclusions with Applications to Machine Learning.
CoRR, 2022

Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD.
CoRR, 2022

A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021
Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions.
IEEE Trans. Autom. Control., 2021

A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Least-Squares Problems.
SIAM J. Sci. Comput., 2021

Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure.
Optim. Lett., 2021

Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity.
Math. Oper. Res., 2021

Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice.
CoRR, 2021

Trilevel and Multilevel Optimization using Monotone Operator Theory.
CoRR, 2021

Decentralized Langevin Dynamics over a Directed Graph.
CoRR, 2021

A zeroth order method for stochastic weakly convex optimization.
Comput. Optim. Appl., 2021

Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization.
SIAM J. Optim., 2020

Second-Order Guarantees of Distributed Gradient Algorithms.
SIAM J. Optim., 2020

Asynchronous parallel algorithms for nonconvex optimization.
Math. Program., 2020

Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems.
J. Optim. Theory Appl., 2020

A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Inverse Problems.
CoRR, 2020

Stochastic Gradient Langevin with Delayed Gradients.
CoRR, 2020

Reinforcement Learning Based on Real-Time Iteration NMPC.
CoRR, 2020

Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent.
CoRR, 2020

Stochastic Gradient Langevin Dynamics on a Distributed Network.
CoRR, 2020

Lifted Weight Learning of Markov Logic Networks (Revisited One More Time).
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

A Two-Step Pre-Processing for Semidefinite Programming.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
Lifted Weight Learning of Markov Logic Networks Revisited.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems.
CoRR, 2018

Algorithms for solving optimization problems arising from deep neural net models: smooth problems.
CoRR, 2018

Second-order Guarantees of Gradient Algorithms over Networks.
Proceedings of the 56th Annual Allerton Conference on Communication, 2018

2017
A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems.
SIAM J. Optim., 2017

A stabilized SQP method: superlinear convergence.
Math. Program., 2017

Essentially cyclic asynchronous nonconvex large-scale optimization.
Proceedings of the 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2017

Capacity sensitivity in additive non-Gaussian noise channels.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

Asynchronous parallel nonconvex large-scale optimization.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016
Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence.
CoRR, 2016

Parallel asynchronous lock-free algorithms for nonconvex big-data optimization.
Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2015
Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization.
IEEE Trans. Signal Process., 2015

2014
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization.
CoRR, 2014

Sequential quadratic programming methods for parametric nonlinear optimization.
Comput. Optim. Appl., 2014

Linear convergence of distributed multiple shooting.
Proceedings of the 13th European Control Conference, 2014

Flexible selective parallel algorithms for big data optimization.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014


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