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Found 7 papers, 0 papers with code
Gradient-based tuning of Hamiltonian Monte Carlo hyperparameters
Existing approaches for automating this task either optimise a proxy for mixing speed or consider the HMC chain as an implicit variational distribution and optimize a tractable lower bound that is too loose to be useful in practice.
The Theory and Algorithm of Ergodic Inference
Approximate inference algorithm is one of the fundamental research fields in machine learning.
Ergodic Measure Preserving Flows
Training probabilistic models with neural network components is intractable in most cases and requires to use approximations such as Markov chain Monte Carlo (MCMC), which is not scalable and requires significant hyper-parameter tuning, or mean-field variational inference (VI), which is biased.
Ergodic Inference: Accelerate Convergence by Optimisation
In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation.
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Sampling from hierarchical Bayesian models is often difficult for MCMC methods, because of the strong correlations between the model parameters and the hyperparameters.
Continuous Relaxations for Discrete Hamiltonian Monte Carlo
Continuous relaxations play an important role in discrete optimization, but have not seen much use in approximate probabilistic inference.
Quasi-Newton Methods for Markov Chain Monte Carlo
The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlations between the random variables of interest.
