Second-derivative sequential quadratic programming methods for nonlinear optimization - Vyacheslav Kungurtsev (2013)

Flexible selective parallel algorithms for big data optimization. - Amir Daneshmand; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (ACSSC, 2014)

Linear convergence of distributed multiple shooting. - Vyacheslav Kungurtsev; Attila Kozma; Moritz Diehl (ECC, 2014)

Sequential quadratic programming methods for parametric nonlinear optimization. - Vyacheslav Kungurtsev; Moritz Diehl (Comput. Optim. Appl., 2014)

Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization. - Amir Daneshmand; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (CoRR, 2014)

Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization. - Amir Daneshmand; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (IEEE Trans. Signal Process., 2015)

Parallel asynchronous lock-free algorithms for nonconvex big-data optimization. - Loris Cannelli; Gesualdo Scutari; Francisco Facchinei; Vyacheslav Kungurtsev (ACSSC, 2016)

Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence. - Loris Cannelli; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (CoRR, 2016)

Asynchronous parallel nonconvex large-scale optimization. - Loris Cannelli; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (ICASSP, 2017)

Capacity sensitivity in additive non-Gaussian noise channels. - Malcolm Egan; Samir Medina Perlaza; Vyacheslav Kungurtsev (ISIT, 2017)

Essentially cyclic asynchronous nonconvex large-scale optimization. - Loris Cannelli; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (SPAWC, 2017)

A stabilized SQP method: superlinear convergence. - Philip E. Gill; Vyacheslav Kungurtsev; Daniel P. Robinson (Math. Program., 2017)

A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems. - Vyacheslav Kungurtsev; Johannes Jäschke (SIAM J. Optim., 2017)

Second-order Guarantees of Gradient Algorithms over Networks. - Amir Daneshmand; Gesualdo Scutari; Vyacheslav Kungurtsev (Allerton, 2018)

Algorithms for solving optimization problems arising from deep neural net models: smooth problems. - Vyacheslav Kungurtsev; Tomás Pevný (CoRR, 2018)

Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems. - Vyacheslav Kungurtsev; Tomás Pevný (CoRR, 2018)

Lifted Weight Learning of Markov Logic Networks Revisited. - Ondrej Kuzelka; Vyacheslav Kungurtsev (AISTATS, 2019)

A Two-Step Pre-Processing for Semidefinite Programming. - Vyacheslav Kungurtsev; Jakub Marecek (CDC, 2020)

Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). - Ondrej Kuzelka; Vyacheslav Kungurtsev; Yuyi Wang 001 (PGM, 2020)

Stochastic Gradient Langevin Dynamics on a Distributed Network. - Vyacheslav Kungurtsev (CoRR, 2020)

Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent. - Dan Alistarh; Bapi Chatterjee; Vyacheslav Kungurtsev (CoRR, 2020)

Reinforcement Learning Based on Real-Time Iteration NMPC. - Mario Zanon; Vyacheslav Kungurtsev; Sébastien Gros (CoRR, 2020)

Stochastic Gradient Langevin with Delayed Gradients. - Vyacheslav Kungurtsev; Bapi Chatterjee; Dan Alistarh (CoRR, 2020)

A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Inverse Problems. - El Houcine Bergou; Youssef Diouane; Vyacheslav Kungurtsev; Clément W. Royer (CoRR, 2020)

Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems. - El Houcine Bergou; Youssef Diouane; Vyacheslav Kungurtsev (J. Optim. Theory Appl., 2020)

Asynchronous parallel algorithms for nonconvex optimization. - Loris Cannelli; Francisco Facchinei; Vyacheslav Kungurtsev; Gesualdo Scutari (Math. Program., 2020)

Second-Order Guarantees of Distributed Gradient Algorithms. - Amir Daneshmand; Gesualdo Scutari; Vyacheslav Kungurtsev (SIAM J. Optim., 2020)

A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. - Philip E. Gill; Vyacheslav Kungurtsev; Daniel P. Robinson (SIAM J. Optim., 2020)

Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees. - Vyacheslav Kungurtsev; Malcolm Egan; Bapi Chatterjee; Dan Alistarh (AAAI, 2021)

Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent. - Giorgi Nadiradze; Ilia Markov; Bapi Chatterjee; Vyacheslav Kungurtsev; Dan Alistarh (AAAI, 2021)

A zeroth order method for stochastic weakly convex optimization. - Vyacheslav Kungurtsev; Francesco Rinaldi (Comput. Optim. Appl., 2021)

Decentralized Langevin Dynamics over a Directed Graph. - Alexander Kolesov; Vyacheslav Kungurtsev (CoRR, 2021)

Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice. - Alberto Schiabel; Vyacheslav Kungurtsev; Jakub Marecek (CoRR, 2021)

Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity. - Francisco Facchinei; Vyacheslav Kungurtsev; Lorenzo Lampariello; Gesualdo Scutari (Math. Oper. Res., 2021)

Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure. - El Houcine Bergou; Youssef Diouane; Vyacheslav Kungurtsev (Optim. Lett., 2021)

A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Least-Squares Problems. - El Houcine Bergou; Youssef Diouane; Vyacheslav Kungurtsev; Clément W. Royer (SIAM J. Sci. Comput., 2021)

Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions. - Loris Cannelli; Francisco Facchinei; Gesualdo Scutari; Vyacheslav Kungurtsev (IEEE Trans. Autom. Control., 2021)

A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization. - Dinesh Krishnamoorthy 002; Vyacheslav Kungurtsev (CDC, 2022)

Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD. - Bapi Chatterjee; Vyacheslav Kungurtsev; Dan Alistarh (CoRR, 2022)

Stochastic Langevin Differential Inclusions with Applications to Machine Learning. - Fabio V. Difonzo; Vyacheslav Kungurtsev; Jakub Marecek (CoRR, 2022)

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. - Alexander Shevchenko; Vyacheslav Kungurtsev; Marco Mondelli (J. Mach. Learn. Res., 2022)

A Stochastic Levenberg-Marquardt Method Using Random Models with Complexity Results. - El Houcine Bergou; Youssef Diouane; Vyacheslav Kungurtsev; Clément W. Royer (SIAM/ASA J. Uncertain. Quantification, 2022)

Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. - Francisco Facchinei; Vyacheslav Kungurtsev; Lorenzo Lampariello; Gesualdo Scutari (Optim. Methods Softw., 2022)

Distributed stochastic nonsmooth nonconvex optimization. - Vyacheslav Kungurtsev (Oper. Res. Lett., 2022)

A Subsampling Line-Search Method with Second-Order Results. - El Houcine Bergou; Youssef Diouane; Vladimir Kunc; Vyacheslav Kungurtsev; Clément W. Royer (INFORMS J. Optim., 2022)

Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. - Saeed Vahidian; Mahdi Morafah; Weijia Wang 002; Vyacheslav Kungurtsev; Chen Chen 001; Mubarak Shah; Bill Lin 001 (AAAI, 2023)

When Do Curricula Work in Federated Learning? - Saeed Vahidian; Sreevatsank Kadaveru; Woonjoon Baek; Weijia Wang 002; Vyacheslav Kungurtsev; Chen Chen 001; Mubarak Shah; Bill Lin 001 (ICCV, 2023)

Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions. - Johannes Aspman; Vyacheslav Kungurtsev; Reza Roohi Seraji (CoRR, 2023)

A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond. - Philip Intallura; Georgios Korpas; Sudeepto Chakraborty; Vyacheslav Kungurtsev; Jakub Marecek (CoRR, 2023)

Quantum Solutions to the Privacy vs. Utility Tradeoff. - Sagnik Chatterjee; Vyacheslav Kungurtsev (CoRR, 2023)

Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation. - Ales Wodecki; Pavel Rytír; Vyacheslav Kungurtsev; Jakub Marecek (CoRR, 2023)

Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. - Vyacheslav Kungurtsev; Adam D. Cobb; Tara Javidi; Brian Jalaian (Mach. Learn., 2023)

Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. - Diyuan Wu; Vyacheslav Kungurtsev; Marco Mondelli (Trans. Mach. Learn. Res., 2023)

On the ergodic control of ensembles in the presence of non-linear filters. - Vyacheslav Kungurtsev; Jakub Marecek; Ramen Ghosh; Robert Shorten (Autom., 2023)

Regularized quasi-monotone method for stochastic optimization. - Vyacheslav Kungurtsev; Vladimir Shikhman (Optim. Lett., 2023)

Decentralized Asynchronous Nonconvex Stochastic Optimization on Directed Graphs. - Vyacheslav Kungurtsev; Mahdi Morafah; Tara Javidi; Gesualdo Scutari (IEEE Trans. Control. Netw. Syst., 2023)

Efficient Dataset Distillation via Minimax Diffusion. - Jianyang Gu; Saeed Vahidian; Vyacheslav Kungurtsev; Haonan Wang; Wei Jiang 009; Yang You 001; Yiran Chen 001 (CVPR, 2024)

Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. - Yuqi Jia; Saeed Vahidian; Jingwei Sun 002; Jianyi Zhang; Vyacheslav Kungurtsev; Neil Zhenqiang Gong; Yiran Chen 001 (ECCV (78), 2024)

Federated SGD with Local Asynchrony. - Bapi Chatterjee; Vyacheslav Kungurtsev; Dan Alistarh (ICDCS, 2024)

Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. - Mahdi Morafah; Vyacheslav Kungurtsev; Hojin Chang; Chen Chen 001; Bill Lin 001 (NeurIPS, 2024)

The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets. - Erwann de Belloy de Saint-Lienard; Jakub Marecek; Vyacheslav Kungurtsev (CoRR, 2024)

Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons. - Vyacheslav Kungurtsev; Petr Rysavý; Fadwa Idlahcen; Pavel Rytír; Ales Wodecki (CoRR, 2024)

Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference. - Vyacheslav Kungurtsev; Apaar; Aarya Khandelwal; Parth Sandeep Rastogi; Bapi Chatterjee; Jakub Marecek (CoRR, 2024)

Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning. - Vyacheslav Kungurtsev; Yuanfang Peng; Jianyang Gu; Saeed Vahidian; Anthony Quinn; Fadwa Idlahcen; Yiran Chen 001 (CoRR, 2024)

Probabilistic Iterative Hard Thresholding for Sparse Learning. - Matteo Bergamaschi; Andrea Cristofari; Vyacheslav Kungurtsev; Francesco Rinaldi (CoRR, 2024)

Iteration Complexity of Variational Quantum Algorithms. - Vyacheslav Kungurtsev; Georgios Korpas; Jakub Marecek; Elton Yechao Zhu (Quantum, 2024)

Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. - Antonio Bellon; Mareike Dressler; Vyacheslav Kungurtsev; Jakub Marecek; André Uschmajew (SIAM J. Optim., 2024)

Trilevel and multilevel optimization using monotone operator theory. - Allahkaram Shafiei; Vyacheslav Kungurtsev; Jakub Marecek (Math. Methods Oper. Res., 2024)

Inexact direct-search methods for bilevel optimization problems. - Youssef Diouane; Vyacheslav Kungurtsev; Francesco Rinaldi; Damiano Zeffiro (Comput. Optim. Appl., 2024)

Retraction-Based Direct Search Methods for Derivative Free Riemannian Optimization. - Vyacheslav Kungurtsev; Francesco Rinaldi; Damiano Zeffiro (J. Optim. Theory Appl., 2024)

Group Distributionally Robust Dataset Distillation with Risk Minimization. - Saeed Vahidian; Mingyu Wang 004; Jianyang Gu; Vyacheslav Kungurtsev; Wei Jiang 009; Yiran Chen 001 (ICLR, 2025)

Parametric Semidefinite Programming: Geometry of the Trajectory of Solutions. - Antonio Bellon; Didier Henrion; Vyacheslav Kungurtsev; Jakub Marecek (Math. Oper. Res., 2025)

A Stochastic-Gradient-Based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. - Frank E. Curtis; Vyacheslav Kungurtsev; Daniel P. Robinson; Qi Wang (SIAM J. Optim., 2025)

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning". - Vyacheslav Kungurtsev; Leonardo Christov Moore; Gustav Sír; Martin Krutsky (CoRR, 2025)

Federated Sinkhorn. - Jeremy Kulcsar; Vyacheslav Kungurtsev; Georgios Korpas; Giulio Giaconi; William Shoosmith (CoRR, 2025)

CONCORD: Concept-Informed Diffusion for Dataset Distillation. - Jianyang Gu; Haonan Wang; Ruoxi Jia 001; Saeed Vahidian; Vyacheslav Kungurtsev; Wei Jiang 009; Yiran Chen 001 (CoRR, 2025)

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations. - Vyacheslav Kungurtsev; Gustav Sir; Akhil S. Anand; Sebastien Gros; Haozhe Tian; Homayoun Hamedmoghadam (CoRR, 2025)

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes. - Kemboi Cheruiyot; Nickson Kiprotich; Vyacheslav Kungurtsev; Kennedy Mugo; Vivian Mwirigi; Marvin Ngesa (CoRR, 2025)

Binarizing Physics-Inspired GNNs for Combinatorial Optimization. - Martin Krutsky; Gustav Sír; Vyacheslav Kungurtsev; Georgios Korpas (CoRR, 2025)