157 citations · 713 across the 56 of their papers we have counts for
44 papers · 1 filter
Generative Modeling by Value-Driven Transport
Pablo Moreno-Muñoz, Adrian Müller, Gergely Neu
We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic results from control theory, we fo…
Online combinatorial optimization with stochastic decision sets and adversarial losses
Gergely Neu, Michal Valko
Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking subsets of readings from senso…
Efficient learning by implicit exploration in bandit problems with side observations
Tomas Kocak, Gergely Neu, Michal Valko +1
We consider online learning problems under a partial observability model capturing situations where the information conveyed to the learner is between full information and bandit f…
Online learning with noisy side observations
Tomáš Kocák, Gergely Neu, Michal Valko
We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback about the other actions, depend…
Sparse Optimistic Information Directed Sampling
Ludovic Schwartz, Hamish Flynn, Gergely Neu
Many high-dimensional online decision-making problems can be modeled as stochastic sparse linear bandits. Most existing algorithms are designed to achieve optimal worst-case regret…
Distances for Markov chains from sample streams
Sergio Calo, Anders Jonsson, Gergely Neu +2
Bisimulation metrics are powerful tools for measuring similarities between stochastic processes, and specifically Markov chains. Recent advances have uncovered that bisimulation me…