3 citations · 3 across the 3 of their papers we have counts for
7 papers
On the Role of Optimization in Double Descent: A Least Squares Study
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata +2
Empirically it has been observed that the performance of deep neural networks steadily improves as we increase model size, contradicting the classical view on overfitting and gener…
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…
A Distribution-Dependent Analysis of Meta-Learning
Mikhail Konobeev, Ilja Kuzborskij, Csaba Szepesvári
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the…
PAC-Bayes Analysis Beyond the Usual Bounds
Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari +1
We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a…
Locally-Adaptive Nonparametric Online Learning
Ilja Kuzborskij, Nicolò Cesa-Bianchi
One of the main strengths of online algorithms is their ability to adapt to arbitrary data sequences. This is especially important in nonparametric settings, where performance is m…
Efron-Stein PAC-Bayesian Inequalities
Ilja Kuzborskij, Csaba Szepesvári
We prove semi-empirical concentration inequalities for random variables which are given as possibly nonlinear functions of independent random variables. These inequalities describe…