71 citations · 151 across the 51 of their papers we have counts for
11 papers · 1 filter
When Do Curricula Work in Federated Learning?
Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek +5
An oft-cited open problem of federated learning is the existence of data heterogeneity at the clients. One pathway to understanding the drastic accuracy drop in federated learning…
A Note on Optimal Tokamak Control for Fusion Power Simulation
M. Holst, V. Kungurtsev, S. Mukherjee
The Tokamak device is the most promising candidate for producing sustainable electric power by nuclear fusion. It is a torus-shaped device that confines plasma by a strong magnetic…
Jump-Diffusion Langevin Dynamics for Multimodal Posterior Sampling
Jacopo Guidolin, Vyacheslav Kungurtsev, Ondřej Kuželka
Bayesian methods of sampling from a posterior distribution are becoming increasingly popular due to their ability to precisely display the uncertainty of a model fit. Classical met…
Time-Varying Multi-Objective Optimization: Tradeoff Regret Bounds
Allahkaram Shafiei, Jakub Marecek
Multi-objective optimization studies the process of seeking multiple competing desiderata in some operation. Solution techniques highlight marginal tradeoffs associated with weighi…
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization
Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev +2
We present an online algorithm for time-varying semidefinite programs (TV-SDPs), based on the tracking of the solution trajectory of a low-rank matrix factorization, also known as…
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence
Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli
The stochastic heavy ball method (SHB), also known as stochastic gradient descent (SGD) with Polyak's momentum, is widely used in training neural networks. However, despite the rem…
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces
Saeed Vahidian, Mahdi Morafah, Weijia Wang +4
Clustered federated learning (FL) has been shown to produce promising results by grouping clients into clusters. This is especially effective in scenarios where separate groups of…
Iteration Complexity of Variational Quantum Algorithms
Vyacheslav Kungurtsev, Georgios Korpas, Jakub Marecek +1
There has been much recent interest in near-term applications of quantum computers, i.e., using quantum circuits that have short decoherence times due to hardware limitations. Vari…
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
Fabio V. Difonzo, Vyacheslav Kungurtsev, Jakub Marecek
Stochastic differential equations of Langevin-diffusion form have received significant attention, thanks to their foundational role in both Bayesian sampling algorithms and optimiz…
Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD
Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
Powered by the simplicity of lock-free asynchrony, Hogwilld! is a go-to approach to parallelize SGD over a shared-memory setting. Despite its popularity and concomitant extensions,…
Retraction based Direct Search Methods for Derivative Free Riemannian Optimization
Vyacheslav Kungurtsev, Francesco Rinaldi, Damiano Zeffiro
Direct search methods represent a robust and reliable class of algorithms for solving black-box optimization problems. In this paper, we explore the application of those strategies…