Johansson, Mikael

19 publications

ICLR 2026 Byzantine-Robust Federated Learning with Learnable Aggregation Weights Javad Parsa, Amir Hossein Daghestani, André M. H. Teixeira, Mikael Johansson

ICLR 2025 An Asynchronous Bundle Method for Distributed Learning Problems Daniel Cederberg, Xuyang Wu, Stephen P. Boyd, Mikael Johansson

ICLR 2025 From Promise to Practice: Realizing High-Performance Decentralized Training Zesen Wang, Jiaojiao Zhang, Xuyang Wu, Mikael Johansson

NeurIPS 2024 Nonconvex Federated Learning on Compact Smooth Submanifolds with Heterogeneous Data Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So, Mikael Johansson

TMLR 2023 Adaptive Hyperparameter Selection for Differentially Private Gradient Descent Dominik Fay, Sindri Magnússon, Jens Sjölund, Mikael Johansson

JMLR 2023 Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees Hamid Reza Feyzmahdavian, Mikael Johansson

NeurIPS 2023 Bringing Regularized Optimal Transport to Lightspeed: A Splitting Method Adapted for GPUs Jacob Lindbäck, Zesen Wang, Mikael Johansson

ICML 2023 Delay-Agnostic Asynchronous Coordinate Update Algorithm Xuyang Wu, Changxin Liu, Sindri Magnússon, Mikael Johansson

ICML 2023 Generalized Polyak Step Size for First Order Optimization with Momentum Xiaoyu Wang, Mikael Johansson, Tong Zhang

ICLR 2022 A Fast and Accurate Splitting Method for Optimal Transport: Analysis and Implementation Vien V. Mai, Jacob Lindbäck, Mikael Johansson

ICML 2022 Delay-Adaptive Step-Sizes for Asynchronous Learning Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian, Mikael Johansson

AAAI 2021 A Flexible Framework for Communication-Efficient Machine Learning Sarit Khirirat, Sindri Magnússon, Arda Aytekin, Mikael Johansson

NeurIPS 2021 On the Convergence of Step Decay Step-Size for Stochastic Optimization Xiaoyu Wang, Sindri Magnússon, Mikael Johansson

ICML 2021 Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness Vien V. Mai, Mikael Johansson

ICML 2020 Anderson Acceleration of Proximal Gradient Methods Vien Mai, Mikael Johansson

ICML 2020 Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization Vien Mai, Mikael Johansson

ICML 2019 Curvature-Exploiting Acceleration of Elastic Net Computations Vien Mai, Mikael Johansson

NeurIPS 2018 Continuous-Time Value Function Approximation in Reproducing Kernel Hilbert Spaces Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama

NeurIPS 2018 The Convergence of Sparsified Gradient Methods Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, Cedric Renggli