Uncertainty
Optimality of variational inference for stochastic block model with missing links
V ariational methods are extremely popular in the analysis of network data.
Matching a Desired Causal State via Shift Interventions
In causal models, such transformations can be achieved by performing a set of interventions.
Model Selection for Bayesian Autoencoders: Supplementary Material Ba-Hien Tran EURECOM (France) Simone Rossi
In this section, we review some key results on the Wasserstein distance. The formulation in Eq. 6 is obtained by employing We use a single multi layer perceptron (MLP) layer with normalized output as the h function. Calculating the Wasserstein distance with the empirical distribution function is computationally attractive. Metropolis steps to accommodate numerical errors stemming from the integration. F .1 Experimental environment In our experiments, we use 4 workstations, which have the following specifications: GPU: NVIDIA Tesla P100 PCIe 16 GB.
Model Selection for Bayesian Autoencoders Ba-Hien Tran
We develop a novel method for carrying out model selection for Bayesian autoen-coders (BAEs) by means of prior hyper-parameter optimization.
Bayesian Inference via Sparse Hamiltonian Flows
Repeated subsampling to reduce cost has the same issues that it does in MCMC.
Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and non-convergence in approximate inference when exact integration and sampling are intractable. However, the computation of a Stein discrepancy can be prohibitive if the Stein operator - often a sum over likelihood terms or potentials - is expensive to evaluate.
On Reward-Free Reinforcement Learning with Linear Function Approximation
During the exploration phase, an agent collects samples without using a pre-specified reward function.