Adaptive Annealed Importance Sampling with Constant Rate Progress
Authors: Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we run a number of experiments to illustrate the performance of CR-AIS on support coverage and adaptivity with 2d distributions and we asses its efficiency and accuracy with estimation of the log normal-ization constant of high dimensional synthetic targets and the posterior of Bayesian models. |
| Researcher Affiliation | Academia | 1Computer Science Department, ETH Zurich, Zurich, Switzerland 2Swiss Data Science Center, Zurich, Switzerland. |
| Pseudocode | Yes | Algorithm 1 CR-AIS tuning for α-divergences |
| Open Source Code | Yes | Code is available at https://github.com/shgoshtasb/cr_ais. |
| Open Datasets | Yes | We use two UCI datasets, Pima Indians diabetes dataset (N = 768 and d = 8) and Sonar dataset (N = 207 and d = 60) with bi-nary labels and a setup similar to (Chopin et al., 2020) with AIS. We estimate the log marginal likelihood of a Variational Auto Encoder trained on binarized MNIST dataset (Salakhutdinov & Murray, 2008). |
| Dataset Splits | No | The paper mentions using 'cross-validation' and describes tuning processes for schedules, but it does not provide explicit details of training/validation/test dataset splits (e.g., percentages or sample counts) for its experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers required for reproducibility. |
| Experiment Setup | Yes | We initialize CR-AIS with a standard normal distribution for q0 and we use the same value of δ = 1/32 for all the targets. Each AIS transition is a single Hamiltonian Monte Carlo (HMC) step with step size 0.5 and N = 1024 particles are used to approximate the constant rate schedule. We set N = 4096 and chose α from a grid in { 0.5, 0.0, ..., 2.}, δ from {1/256, ..., 4096} for CR-AIS depending on the target... We vary δ between {64, ..., 2048} for CR-AIS and maximum step size in the range {1/65536, ..., 1/2048} for Adaptive AIS with N = 256 particles. |