6 citations · 6 across the 6 of their papers we have counts for
6 papers
Radius of Information for Two Intersected Centered Hyperellipsoids and Implications in Optimal Recovery from Inaccurate Data
Simon Foucart, Chunyang Liao
For objects belonging to a known model set and observed through a prescribed linear process, we aim at determining methods to recover linear quantities of these objects that are op…
S-Procedure Relaxation: a Case of Exactness Involving Chebyshev Centers
Simon Foucart, Chunyang Liao
Optimal recovery is a mathematical framework for learning functions from observational data by adopting a worst-case perspective tied to model assumptions on the functions to be le…
On the Optimal Recovery of Graph Signals
Simon Foucart, Chunyang Liao, Nate Veldt
Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery,…
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
Mingrui Liu, Zhenxun Zhuang, Yunwei Lei +1
In distributed training of deep neural networks, people usually run Stochastic Gradient Descent (SGD) or its variants on each machine and communicate with other machines periodical…
Optimal Recovery from Inaccurate Data in Hilbert Spaces: Regularize, but what of the Parameter?
Simon Foucart, Chunyang Liao
In Optimal Recovery, the task of learning a function from observational data is tackled deterministically by adopting a worst-case perspective tied to an explicit model assumption…
Learning from Non-Random Data in Hilbert Spaces: An Optimal Recovery Perspective
Simon Foucart, Chunyang Liao, Shahin Shahrampour +1
The notion of generalization in classical Statistical Learning is often attached to the postulate that data points are independent and identically distributed (IID) random variable…