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Vyacheslav Kungurtsev
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2020 – today
- 2024
- [j26]Youssef Diouane
, Vyacheslav Kungurtsev, Francesco Rinaldi, Damiano Zeffiro
Inexact direct-search methods for bilevel optimization problems. Comput. Optim. Appl. 88(2): 469-490 (2024) - [j25]Allahkaram Shafiei
Trilevel and multilevel optimization using monotone operator theory. Math. Methods Oper. Res. 99(1): 77-114 (2024) - [j24]Antonio Bellon, Mareike Dressler
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. SIAM J. Optim. 34(1): 1-26 (2024) - [c18]Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen:
Efficient Dataset Distillation via Minimax Diffusion. CVPR 2024: 15793-15803 - [c17]Yuqi Jia, Saeed Vahidian
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. ECCV (78) 2024: 18-33 - [c16]Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh:
Federated SGD with Local Asynchrony. ICDCS 2024: 857-868 - [i36]Erwann de Belloy de Saint-Lienard, Jakub Marecek, Vyacheslav Kungurtsev:
The Effects of Transmission-Rights Pricing on Multi-Stage Electricity Markets. CoRR abs/2401.15772 (2024) - [i35]Saeed Vahidian, Mingyu Wang, Jianyang Gu
Group Distributionally Robust Dataset Distillation with Risk Minimization. CoRR abs/2402.04676 (2024) - [i34]Vyacheslav Kungurtsev, Petr Rysavy, Fadwa Idlahcen, Pavel Rytír, Ales Wodecki:
Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons. CoRR abs/2406.17585 (2024) - [i33]Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi, Bapi Chatterjee, Jakub Marecek:
Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference. CoRR abs/2406.17831 (2024) - [i32]Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu, Saeed Vahidian, Anthony Quinn, Fadwa Idlahcen, Yiran Chen:
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning. CoRR abs/2409.01410 (2024) - [i31]Matteo Bergamaschi, Andrea Cristofari, Vyacheslav Kungurtsev, Francesco Rinaldi:
Probabilistic Iterative Hard Thresholding for Sparse Learning. CoRR abs/2409.01413 (2024) - [i30]Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang, Chen Chen, Bill Lin:
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. CoRR abs/2409.18461 (2024) - 2023
- [j23]Vyacheslav Kungurtsev
On the ergodic control of ensembles in the presence of non-linear filters. Autom. 152: 110946 (2023) - [j22]Vyacheslav Kungurtsev
Decentralized Bayesian learning with Metropolis-adjusted Hamiltonian Monte Carlo. Mach. Learn. 112(8): 2791-2819 (2023) - [j21]Vyacheslav Kungurtsev
Regularized quasi-monotone method for stochastic optimization. Optim. Lett. 17(5): 1215-1228 (2023) - [j20]Vyacheslav Kungurtsev
Decentralized Asynchronous Nonconvex Stochastic Optimization on Directed Graphs. IEEE Trans. Control. Netw. Syst. 10(4): 1796-1804 (2023) - [j19]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) - [c15]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: 10043-10052 - [c14]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: 5061-5071 - [i29]Johannes Aspman
Riemannian Stochastic Approximation for Minimizing Tame Nonsmooth Objective Functions. CoRR abs/2302.00709 (2023) - [i28]Philip Intallura, Georgios Korpas, Sudeepto Chakraborty, Vyacheslav Kungurtsev
A Survey of Quantum Alternatives to Randomized Algorithms: Monte Carlo Integration and Beyond. CoRR abs/2303.04945 (2023) - [i27]Frank E. Curtis, Vyacheslav Kungurtsev
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. CoRR abs/2304.14907 (2023) - [i26]Sagnik Chatterjee
Quantum Solutions to the Privacy vs. Utility Tradeoff. CoRR abs/2307.03118 (2023) - [i25]Jianyang Gu
Efficient Dataset Distillation via Minimax Diffusion. CoRR abs/2311.15529 (2023) - [i24]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 abs/2312.01537 (2023) - [i23]Ales Wodecki, Pavel Rytír, Vyacheslav Kungurtsev, Jakub Marecek:
Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation. CoRR abs/2312.11381 (2023) - 2022
- [j18]El Houcine Bergou
A Subsampling Line-Search Method with Second-Order Results. INFORMS J. Optim. 4(4): 403-425 (2022) - [j17]Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. J. Mach. Learn. Res. 23: 130:1-130:55 (2022) - [j16]El Houcine Bergou, Youssef Diouane
A Stochastic Levenberg-Marquardt Method Using Random Models with Complexity Results. SIAM/ASA J. Uncertain. Quantification 10(1): 507-536 (2022) - [j15]Francisco Facchinei, Vyacheslav Kungurtsev
Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case. Optim. Methods Softw. 37(4): 1242-1268 (2022) - [j14]Vyacheslav Kungurtsev
Distributed stochastic nonsmooth nonconvex optimization. Oper. Res. Lett. 50(6): 627-631 (2022) - [c13]Dinesh Krishnamoorthy, Vyacheslav Kungurtsev
A Sensitivity Assisted Alternating Directions Method of Multipliers for Distributed Optimization. CDC 2022: 295-300 - [i22]Bapi Chatterjee, Vyacheslav Kungurtsev
Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD. CoRR abs/2203.06638 (2022) - [i21]Fabio V. Difonzo, Vyacheslav Kungurtsev
Stochastic Langevin Differential Inclusions with Applications to Machine Learning. CoRR abs/2206.11533 (2022) - [i20]Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces. CoRR abs/2209.10526 (2022) - [i19]Diyuan Wu, Vyacheslav Kungurtsev
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. CoRR abs/2210.06819 (2022) - [i18]Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. CoRR abs/2210.08387 (2022) - [i17]Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev
When Do Curricula Work in Federated Learning? CoRR abs/2212.12712 (2022) - 2021
- [j13]Vyacheslav Kungurtsev
A zeroth order method for stochastic weakly convex optimization. Comput. Optim. Appl. 80(3): 731-753 (2021) - [j12]Francisco Facchinei
Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity. Math. Oper. Res. 46(2): 595-627 (2021) - [j11]El Houcine Bergou, Youssef Diouane
Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure. Optim. Lett. 15(4): 1215-1227 (2021) - [j10]El Houcine Bergou, Youssef Diouane
A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Least-Squares Problems. SIAM J. Sci. Comput. 43(5): S743-S766 (2021) - [j9]Loris Cannelli
Asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions. IEEE Trans. Autom. Control. 66(10): 4604-4619 (2021) - [c12]Vyacheslav Kungurtsev, Malcolm Egan, Bapi Chatterjee, Dan Alistarh:
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees. AAAI 2021: 8209-8216 - [c11]Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh:
Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent. AAAI 2021: 9037-9045 - [i16]Alexander Kolesov, Vyacheslav Kungurtsev:
Decentralized Langevin Dynamics over a Directed Graph. CoRR abs/2103.05444 (2021) - [i15]Allahkaram Shafiei, Vyacheslav Kungurtsev, Jakub Marecek:
Trilevel and Multilevel Optimization using Monotone Operator Theory. CoRR abs/2105.09407 (2021) - [i14]Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian:
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo. CoRR abs/2107.07211 (2021) - [i13]Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. CoRR abs/2111.02278 (2021) - [i12]Alberto Schiabel, Vyacheslav Kungurtsev, Jakub Marecek:
Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Lattice. CoRR abs/2111.10175 (2021) - 2020
- [j8]El Houcine Bergou, Youssef Diouane
Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems. J. Optim. Theory Appl. 185(3): 927-944 (2020) - [j7]Loris Cannelli
Asynchronous parallel algorithms for nonconvex optimization. Math. Program. 184(1): 121-154 (2020) - [j6]Philip E. Gill, Vyacheslav Kungurtsev
A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. SIAM J. Optim. 30(2): 1067-1093 (2020) - [j5]Amir Daneshmand, Gesualdo Scutari
Second-Order Guarantees of Distributed Gradient Algorithms. SIAM J. Optim. 30(4): 3029-3068 (2020) - [c10]Vyacheslav Kungurtsev
A Two-Step Pre-Processing for Semidefinite Programming. CDC 2020: 384-389 - [c9]Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang:
Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM 2020: 269-280 - [i11]Vyacheslav Kungurtsev:
Stochastic Gradient Langevin Dynamics on a Distributed Network. CoRR abs/2001.00665 (2020) - [i10]Dan Alistarh, Bapi Chatterjee, Vyacheslav Kungurtsev:
Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent. CoRR abs/2001.05918 (2020) - [i9]Mario Zanon, Vyacheslav Kungurtsev, Sébastien Gros:
Reinforcement Learning Based on Real-Time Iteration NMPC. CoRR abs/2005.05225 (2020) - [i8]Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh:
Stochastic Gradient Langevin with Delayed Gradients. CoRR abs/2006.07362 (2020) - [i7]El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev, Clément W. Royer:
A Nonmonotone Matrix-Free Algorithm for Nonlinear Equality-Constrained Inverse Problems. CoRR abs/2006.16340 (2020)
2010 – 2019
- 2019
- [c8]Ondrej Kuzelka, Vyacheslav Kungurtsev:
Lifted Weight Learning of Markov Logic Networks Revisited. AISTATS 2019: 1753-1761 - [i6]Ondrej Kuzelka, Vyacheslav Kungurtsev:
Lifted Weight Learning of Markov Logic Networks Revisited. CoRR abs/1903.03099 (2019) - 2018
- [c7]Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
Second-order Guarantees of Gradient Algorithms over Networks. Allerton 2018: 359-365 - [i5]Vyacheslav Kungurtsev, Tomás Pevný:
Algorithms for solving optimization problems arising from deep neural net models: smooth problems. CoRR abs/1807.00172 (2018) - [i4]Vyacheslav Kungurtsev, Tomás Pevný:
Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems. CoRR abs/1807.00173 (2018) - [i3]Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev:
Second-order Guarantees of Distributed Gradient Algorithms. CoRR abs/1809.08694 (2018) - 2017
- [j4]Philip E. Gill, Vyacheslav Kungurtsev
A stabilized SQP method: superlinear convergence. Math. Program. 163(1-2): 369-410 (2017) - [j3]Vyacheslav Kungurtsev
A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems. SIAM J. Optim. 27(1): 538-564 (2017) - [c6]Loris Cannelli
Asynchronous parallel nonconvex large-scale optimization. ICASSP 2017: 4706-4710 - [c5]Malcolm Egan, Samir Medina Perlaza, Vyacheslav Kungurtsev
Capacity sensitivity in additive non-Gaussian noise channels. ISIT 2017: 416-420 - [c4]Loris Cannelli
Essentially cyclic asynchronous nonconvex large-scale optimization. SPAWC 2017: 1-5 - 2016
- [c3]Loris Cannelli
Parallel asynchronous lock-free algorithms for nonconvex big-data optimization. ACSSC 2016: 1009-1013 - [i2]Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari:
Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence. CoRR abs/1607.04818 (2016) - 2015
- [j2]Amir Daneshmand, Francisco Facchinei
Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization. IEEE Trans. Signal Process. 63(15): 3914-3929 (2015) - 2014
- [j1]Vyacheslav Kungurtsev
Sequential quadratic programming methods for parametric nonlinear optimization. Comput. Optim. Appl. 59(3): 475-509 (2014) - [c2]Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev
Flexible selective parallel algorithms for big data optimization. ACSSC 2014: 3-7 - [c1]Vyacheslav Kungurtsev
Linear convergence of distributed multiple shooting. ECC 2014: 2874-2879 - [i1]Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev, Gesualdo Scutari:
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization. CoRR abs/1407.4504 (2014) - 2013
- [b1]Vyacheslav Kungurtsev:
Second-derivative sequential quadratic programming methods for nonlinear optimization. University of California, San Diego, USA, 2013
Coauthor Index
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last updated on 2024-11-07 20:36 CET by the dblp team
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