160 citations · 264 across the 9 of their papers we have counts for
3 papers · 1 filter
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Nicolas Brosse, Carlos Riquelme, Alice Martin +2
Uncertainty quantification for deep learning is a challenging open problem. Bayesian statistics offer a mathematically grounded framework to reason about uncertainties; however, ap…
Practical and Consistent Estimation of f-Divergences
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga +2
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning. Most works study this problem…
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
Carlos Riquelme, George Tucker, Jasper Snoek
Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to bal…