280 citations · 2k across the 81 of their papers we have counts for
4 papers · 2 filters
Infinite-Horizon Gaussian Processes
Arno Solin, James Hensman, Richard E. Turner
Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting long temporal datasets. State space modelling (Kalman filtering) enables these non…
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds +3
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probab…
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani +2
We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines…
The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu +3
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Seve…