12 citations · 26 across the 22 of their papers we have counts for
6 papers · 1 filter
Stochastic Shortest Path with Sparse Adversarial Costs
Emmeran Johnson, Alberto Rumi, Ciara Pike-Burke +1
We study the adversarial Stochastic Shortest Path (SSP) problem with sparse costs under full-information feedback. In the known transition setting, existing bounds based on Online…
Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis
Tyler Farghly, Patrick Rebeschini, George Deligiannidis +1
The success of denoising diffusion models raises important questions regarding their generalisation behaviour, particularly in high-dimensional settings. Notably, it has been shown…
On the necessity of adaptive regularisation:Optimal anytime online learning on -balls
Emmeran Johnson, David Martínez-Rubio, Ciara Pike-Burke +1
We study online convex optimization on -balls in for . While always sub-linear, the optimal regret exhibits a shift between the high-dimensional setti…
Non-stationary Bandit Convex Optimization: A Comprehensive Study
Xiaoqi Liu, Dorian Baudry, Julian Zimmert +2
Bandit Convex Optimization is a fundamental class of sequential decision-making problems, where the learner selects actions from a continuous domain and observes a loss (but not it…
Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression
Tobias Wegel, Gil Kur, Patrick Rebeschini
We study early-stopped mirror descent (ESMD) for high-dimensional Gaussian linear regression over arbitrary convex bodies and design matrices, where the task is to minimize the in-…
Uniform mean estimation for monotonic processes
Eugenio Clerico, Hamish E Flynn, Patrick Rebeschini
We consider the problem of deriving uniform confidence bands for the mean of a monotonic stochastic process, such as the cumulative distribution function (CDF) of a random variable…