4 papers
Beyond Softmax: A New Perspective on Gradient Bandits
Emerson Melo, David Müller
We establish a link between a class of discrete choice models and the theory of online learning and multi-armed bandits. Our contributions are: (i) sublinear regret bounds for a br…
Learning in Random Utility Models Via Online Decision Problems
Emerson Melo
This paper examines the Random Utility Model (RUM) in repeated stochastic choice settings where decision-makers lack full information about payoffs. We propose a gradient-based lea…
Discrete Choice Multi-Armed Bandits
Emerson Melo, David Müller
This paper establishes a connection between a category of discrete choice models and the realms of online learning and multiarmed bandit algorithms. Our contributions can be summar…
A Distributionally Robust Random Utility Model
David Müller, Emerson Melo, Ruben Schlotter
This paper introduces the distributionally robust random utility model (DRO-RUM), which allows the preference shock (unobserved heterogeneity) distribution to be misspecified or un…