# Accelerating Stochastic Probabilistic Inference

arXiv: https://arxiv.org/abs/2203.07585 · HTML: https://mlsanity.com/paper/2203.07585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Stochastic Gradient Hamiltonian Monte Carlo](https://mlsanity.com/paper/1402.4102/) (2014)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators](https://mlsanity.com/paper/1610.06665/) (2016)
- [Second-Order Stochastic Optimization for Machine Learning in Linear Time](https://mlsanity.com/paper/1602.03943/) (2016)
- [Fast Second-Order Stochastic Backpropagation for Variational Inference](https://mlsanity.com/paper/1509.02866/) (2015)

## Abstract (arXiv)

Recently, Stochastic Variational Inference (SVI) has been increasingly attractive thanks to its ability to find good posterior approximations of probabilistic models. It optimizes the variational objective with stochastic optimization, following noisy estimates of the natural gradient. However, almost all the state-of-the-art SVI algorithms are based on first-order optimization algorithm and often suffer from poor convergence rate. In this paper, we bridge the gap between second-order methods and stochastic variational inference by proposing a second-order based stochastic variational inference approach. In particular, firstly we derive the Hessian matrix of the variational objective. Then we devise two numerical schemes to implement second-order SVI efficiently. Thorough empirical evaluations are investigated on both synthetic and real dataset to backup both the effectiveness and efficiency of the proposed approach.