139 citations · 273 across the 16 of their papers we have counts for
16 papers
Causal Bandits: Learning Good Interventions via Causal Inference
Finnian Lattimore, Tor Lattimore, Mark D. Reid
We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm band…
Compliance-Aware Bandits
Nicolás Della Penna, Mark D. Reid, David Balduzzi
Motivated by clinical trials, we study bandits with observable non-compliance. At each step, the learner chooses an arm, after, instead of observing only the reward, it also observ…
Fast rates in statistical and online learning
Tim van Erven, Peter D. Grünwald, Nishant A. Mehta +2
The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is nee…
Risk Dynamics in Trade Networks
Rafael M. Frongillo, Mark D. Reid
We introduce a new framework to model interactions among agents which seek to trade to minimize their risk with respect to some future outcome. We quantify this risk using the conc…
Generalized Mixability via Entropic Duality
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson +1
Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice. We show that a key property of mixability gen…
Generalised Mixability, Constant Regret, and Bayesian Updating
Mark D. Reid, Rafael M. Frongillo, Robert C. Williamson
Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm.…