20 citations · 49 across the 10 of their papers we have counts for
5 papers · 1 filter
Sparse Gaussian Processes with Spherical Harmonic Features Revisited
Stefanos Eleftheriadis, Dominic Richards, James Hensman
We revisit the Gaussian process model with spherical harmonic features and study connections between the associated RKHS, its eigenstructure and deep models. Based on this, we intr…
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel
Dominic Richards, Ilja Kuzborskij
We revisit on-average algorithmic stability of GD for training overparameterised shallow neural networks and prove new generalisation and excess risk bounds without the NTK or PL a…
Learning with Gradient Descent and Weakly Convex Losses
Dominic Richards, Mike Rabbat
We study the learning performance of gradient descent when the empirical risk is weakly convex, namely, the smallest negative eigenvalue of the empirical risk's Hessian is bounded…
Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
We investigate the generalisation performance of Distributed Gradient Descent with Implicit Regularisation and Random Features in the homogenous setting where a network of agents a…
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up
Dominic Richards, Patrick Rebeschini
We analyse the learning performance of Distributed Gradient Descent in the context of multi-agent decentralised non-parametric regression with the square loss function when i.i.d.…