Uncertainty
Natural gradient enables fast sampling in spiking neural networks
For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions.
Safe Policy Optimization with Local Generalized Linear Function Approximations
We propose a novel algorithm, SPO-LF, that optimizes an agent's policy while
Relaxed Marginal Consistency for Differentially Private Query Answering
MWEM [5] to scale to far more measurements, and improves the accuracy of FEM [33].
Supplement for Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows A Subsplit Bayesian networks D AB C AB CD ABC D ABC D AB CD D AB CD AB CD AB CD assign
Figure 1: A simple subsplit Bayesian network for a leaf set that contains 4 species A, B, C and D. This figure is adapted from Zhang and Matsen IV (2019). SBN (the one with a full and complete binary tree structure as shown in Figure 1) is good enough. The SBN framework also generalizes to unrooted trees, which are the most common type of phylogenetic trees. (Zhang and Matsen IV, 2018). Sampling from SBNs is also straightforward via ancestral sampling.
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
We show that VBPI-NF significantly improves upon the vanilla VBPI on a benchmark of challenging real data Bayesian phylogenetic inference problems.
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly.
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Prescott and Baker (2020) propose a multi-fidelity approach to ABC. In our work, the Russian roulette estimator is used to construct an unbiased, low-fidelity likelihood.
