2 citations · 2 across the 8 of their papers we have counts for
9 papers
Robustifying Asynchronous SGD via Soft Throttling
Kaoru Otsuka, Maxime Meyer, Yuki Takezawa +2
Asynchronous SGD is a popular algorithm for distributed learning where each client's gradient update is applied on arrival. This leads to a speed-up, but also an increased vulnerab…
On Optimization Complexity of Second-Order Certified Unlearning
Nikita Doikov, Anastasia Koloskova
We study machine unlearning: the removal of memorized training data from a trained model. Specifically, we investigate the algorithmic complexity of certified unlearning from an op…
Auditing of Unlearning Algorithms
Sahasrajit Sarmasarkar, Anastasia Koloskova, Sanmi Koyejo
Evaluating whether unlearning algorithms truly remove training data influence remains an open challenge. We propose a practical auditor that computes data-dependent lower bounds on…
Improved Convergence Analysis of Topology Dependence in Decentralized SGD
Yuki Takezawa, Anastasia Koloskova, Sebastian U. Stich
Decentralized SGD is a fundamental algorithm in decentralized learning, although the influence of an underlying network topology on its convergence behavior is not yet fully unders…
Avoiding Bias in Clipped SGD for Overparameterized Models under Generalized Smoothness
Aleksandr Lobanov, Anastasia Koloskova
Modern machine learning is dominated by complex, overparameterized architectures capable of interpolating data and achieving zero training loss. For such models, we investigate the…
FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
Yuki Takezawa, Anastasia Koloskova, Xiaowen Jiang +1
Recently, a new optimization method based on the linear minimization oracle (LMO), called Muon, has been attracting increasing attention since it can train neural networks faster t…
Certified Unlearning for Neural Networks
Anastasia Koloskova, Youssef Allouah, Animesh Jha +2
We address the problem of machine unlearning, where the goal is to remove the influence of specific training data from a model upon request, motivated by privacy concerns and regul…
The Privacy Power of Correlated Noise in Decentralized Learning
Youssef Allouah, Anastasia Koloskova, Aymane El Firdoussi +2
Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting p…
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization
Mathieu Even, Anastasia Koloskova, Laurent Massoulié
Decentralized and asynchronous communications are two popular techniques to speedup communication complexity of distributed machine learning, by respectively removing the dependenc…