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Found 14 papers, 5 papers with code
Joint control variate for faster black-box variational inference
Black-box variational inference performance is sometimes hindered by the use of gradient estimators with high variance.
Compositional Score Modeling for Simulation-based Inference
Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as they tend to require a large number of simulator calls to learn accurate approximations.
Langevin Diffusion Variational Inference
In fact, using our formulation we propose a new method that combines the strengths of previously existing algorithms; it uses underdamped Langevin transitions and powerful augmentations parameterized by a score network.
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Hierarchical models represent a challenging setting for inference algorithms.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
FCause: Flow-based Causal Discovery
Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values.
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Given an unnormalized target distribution we want to obtain approximate samples from it and a tight lower bound on its (log) normalization constant log Z. Annealed Importance Sampling (AIS) with Hamiltonian MCMC is a powerful method that can be used to do this.
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization
In this paper we empirically evaluate biased methods for alpha-divergence minimization.
On the Difficulty of Unbiased Alpha Divergence Minimization
In this work we study unbiased methods for alpha-divergence minimization through the Signal-to-Noise Ratio (SNR) of the gradient estimator.
Approximation Based Variance Reduction for Reparameterization Gradients
Flexible variational distributions improve variational inference but are harder to optimize.
A Rule for Gradient Estimator Selection, with an Application to Variational Inference
Inspired by this principle, we propose a technique to automatically select an estimator when a finite pool of estimators is given.
Automatically Trading off Time and Variance when Selecting Gradient Estimators
Inspired by this principle, we propose a technique to automatically select an estimator when a finite pool of estimators is given.
Using Large Ensembles of Control Variates for Variational Inference
Variational inference is increasingly being addressed with stochastic optimization.
Compact Policies for Fully-Observable Non-Deterministic Planning as SAT
Fully observable non-deterministic (FOND) planning is becoming increasingly important as an approach for computing proper policies in probabilistic planning, extended temporal plans in LTL planning, and general plans in generalized planning.
