649 citations
- Carnegie Mellon UniversityUS23 papers
- Stanford UniversityUS20 papers
- Google (United States)US14 papers
- Georgia Institute of TechnologyUS13 papers
- Tel Aviv UniversityIL12 papers
- Cornell UniversityUS11 papers
- University of California, BerkeleyUS11 papers
- University College LondonGB10 papers
- Harvard University PressUS9 papers
- Johns Hopkins UniversityUS9 papers
- Massachusetts Institute of TechnologyUS9 papers
- The University of Texas at AustinUS9 papers
14 papers · 1 filter
TyXe: Pyro-based Bayesian neural nets for Pytorch
Hippolyt Ritter, Theofanis Karaletsos
We introduce TyXe, a Bayesian neural network library built on top of Pytorch and Pyro. Our leading design principle is to cleanly separate architecture, prior, inference and likeli…
Moser Flow: Divergence-based Generative Modeling on Manifolds
Noam Rozen, Aditya Grover, Maximilian Nickel +1
We are interested in learning generative models for complex geometries described via manifolds, such as spheres, tori, and other implicit surfaces. Current extensions of existing (…
Localized Uncertainty Attacks
Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas +3
The susceptibility of deep learning models to adversarial perturbations has stirred renewed attention in adversarial examples resulting in a number of attacks. However, most of the…
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Marc Abeille, Alessandro Lazaric
We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting. Inspired by the extended value iteration algorithm used in optimistic algorithms for…
Meta-learning with Stochastic Linear Bandits
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
We investigate meta-learning procedures in the setting of stochastic linear bandits tasks. The goal is to select a learning algorithm which works well on average over a class of ba…
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are one of the most successful frameworks to model uncertainty. However, GP optimization (e.g., GP-UCB) suffers from major scalability issues. Experimental…