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..
Amortized Monte Carlo Integration
Authors: Adam Golinski, Frank Wood, Tom Rainforth
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | It is therefore necessary to test its empirical performance to assert that gains are possible with inexact proposals. To this end, we investigate AMCI s performance on two illustrative examples. [...] As shown in Figure 1, AMCI outperformed SNIS in both the one- and five-dimensional cases. [...] Results are presented in Figure 2. AMCI again significantly outperformed the literature baseline of SNIS q2 |
| Researcher Affiliation | Academia | Adam Goli nski * 1 2 Frank Wood 3 Tom Rainforth * 1 1Department of Statistics, University of Oxford, United Kingdom 2Department of Engineering Science, University of Oxford, United Kingdom 3Department of Computer Science, University of British Columbia, Vancouver, Canada. |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | An implementation for AMCI and our experiments is available at http://github.com/talesa/amci. |
| Open Datasets | No | We start with the conceptually simple problem of calculating tail integrals for Gaussian distributions, namely p(x) = N(x; 0, Σ1) p(y|x) = N(y; x, Σ2) (24) f(x; θ) = YD i=1 1xi>θi p(θ) = UNIFORM(θ; [0, u D]D where D is the dimensionality, we set Σ2 = I, and Σ1 is a fixed covariance matrix (for details see Appendix C). [...] To demonstrate how AMCI might be used in a more realworld scenario, we now consider an illustrative example relating to cancer diagnostic decisions. [...] A detailed description of the model and proposal setup is in the Appendix C.3. |
| Dataset Splits | No | Though the exact process varies with context, the inference network is usually trained either by drawing latent-data sample pairs from the joint p(x, y) (Paige & Wood, 2016; Le et al., 2017; 2018b), or by drawing mini-batches from a large dataset using stochastic variational inference approaches (Hoffman et al., 2013; Kingma & Welling, 2014; Rezende et al., 2014; Ritchie et al., 2016). |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or specific computing environments) are provided for running the experiments. |
| Software Dependencies | No | We use normalizing flows (Rezende & Mohamed, 2015) to construct our proposals, providing a flexible and powerful means of representing the target distributions. |
| Experiment Setup | No | Training was done by using importance sampling to generate the values of θ and x as per (22) with q (θ, x) = p(θ) HALFNORMAL(x; θ, diag(Σ2)). |