Kungurtsev, Vyacheslav

14 publications

ICLR 2025 Group Distributionally Robust Dataset Distillation with Risk Minimization Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang, Yiran Chen

CVPR 2024 Efficient Dataset Distillation via Minimax Diffusion Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen

NeurIPS 2024 Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang, Chen Chen, Bill Lin

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

MLJ 2023 Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev, Adam D. Cobb, Tara Javidi, Brian Jalaian

AAAI 2023 Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin

TMLR 2023 Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli

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

NeurIPSW 2022 Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli

JMLR 2022 Mean-Field Analysis of Piecewise Linear Solutions for Wide ReLU Networks Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli

AAAI 2021 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

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

AISTATS 2019 Lifted Weight Learning of Markov Logic Networks Revisited Ondrej Kuzelka, Vyacheslav Kungurtsev