From the 1 of 1.5k papers with an AI index.
24.4k citations
- University of California, BerkeleyUS82 papers
- Stanford UniversityUS72 papers
- Massachusetts Institute of TechnologyUS56 papers
- Google DeepMind (United Kingdom)GB51 papers
- Carnegie Mellon UniversityUS49 papers
- University of TorontoCA37 papers
- Princeton UniversityUS33 papers
- Cornell UniversityUS29 papers
- University of OxfordGB26 papers
- University of WashingtonUS26 papers
- University of Illinois Urbana-ChampaignUS24 papers
- Columbia UniversityUS23 papers
74 papers · 1 filter
Variational Bayesian Optimistic Sampling
Brendan O'Donoghue, Tor Lattimore
We consider online sequential decision problems where an agent must balance exploration and exploitation. We derive a set of Bayesian `optimistic' policies which, in the stochastic…
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu +2
The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling th…
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Guodong Zhang, Kyle Hsu, Jianing Li +2
Annealed importance sampling (AIS) and related algorithms are highly effective tools for marginal likelihood estimation, but are not fully differentiable due to the use of Metropol…
Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin +1
Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity ori…
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
Ghassen Jerfel, Serena Wang, Clara Fannjiang +3
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler diver…
Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints
Wei-Ning Chen, Peter Kairouz, Ayfer Özgür
We consider the problem of estimating a -dimensional -sparse discrete distribution from its samples observed under a -bit communication constraint. The best-known previous…