2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
Sergio Calo, Anders Jonsson, Gergely Neu +2
We propose a new framework for formulating optimal transport distances between Markov chains. Previously known formulations studied couplings between the entire joint distribution…
Optimistic Information Directed Sampling
Gergely Neu, Matteo Papini, Ludovic Schwartz
We study the problem of online learning in contextual bandit problems where the loss function is assumed to belong to a known parametric function class. We propose a new analytic f…
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
Gergely Neu, Julia Olkhovskaya, Matteo Papini +1
We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theo…
Efficient conditioned face animation using frontally-viewed embedding
Maxime Oquab, Daniel Haziza, Ludovic Schwartz +5
As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of rea…