Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Annealed Importance Sampling with Constant Rate Progress
Authors: Shirin Goshtasbpour, Victor Cohen, Fernando Perez-Cruz
ICML 2023 | Venue PDF | 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. |