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9 papers · 2 filters
Rotting bandits are not harder than stochastic ones
Julien Seznec, Andrea Locatelli, Alexandra Carpentier +2
In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary. This assumption is often violated in practice (e.g., in recommendation systems),…
Resampled Priors for Variational Autoencoders
Matthias Bauer, Andriy Mnih
We propose Learned Accept/Reject Sampling (LARS), a method for constructing richer priors using rejection sampling with a learned acceptance function. This work is motivated by rec…
Taming VAEs
Danilo Jimenez Rezende, Fabio Viola
In spite of remarkable progress in deep latent variable generative modeling, training still remains a challenge due to a combination of optimization and generalization issues. In p…
Unbiased Implicit Variational Inference
Michalis K. Titsias, Francisco J. R. Ruiz
We develop unbiased implicit variational inference (UIVI), a method that expands the applicability of variational inference by defining an expressive variational family. UIVI consi…
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki +4
We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters an…
Linear Bandits with Stochastic Delayed Feedback
Claire Vernade, Alexandra Carpentier, Tor Lattimore +3
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and rec…