3 citations · 7 across the 7 of their papers we have counts for
8 papers · 1 filter
Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators
Tom Blau, Iadine Chades, Amir Dezfouli +2
Reinforcement learning can learn amortised design policies for designing sequences of experiments. However, current amortised methods rely on estimators of expected information gai…
Parameter Estimation in DAGs from Incomplete Data via Optimal Transport
Vy Vo, Trung Le, Tung-Long Vuong +3
Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge. This is because, in the presence of latent variables, both…
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Pantelis Elinas, Edwin V. Bonilla
Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task. It is known that deep GNNs suffer from over-smoothing where, as the number o…
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Weiming Zhi, Tin Lai, Lionel Ott +2
Advances in differentiable numerical integrators have enabled the use of gradient descent techniques to learn ordinary differential equations (ODEs). In the context of machine lear…
BORE: Bayesian Optimization by Density-Ratio Estimation
Louis C. Tiao, Aaron Klein, Matthias Seeger +3
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion enco…
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang, Christian J. Walder, Edwin V. Bonilla +2
Approximate inference techniques are the cornerstone of probabilistic methods based on Gaussian process priors. Despite this, most work approximately optimizes standard divergence…