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..
Counterfactual Density Estimation using Kernel Stein Discrepancies
Authors: Diego Martinez-Taboada, Edward Kennedy
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | First, we present a novel estimator for modelling counterfactual distributions given a parametric class of distributions, along with its theoretical analysis. Second, we illustrate the empirical performance of the estimator in a variety of scenarios. and 5 EXPERIMENTS We provide a number of experiments with (semi)synthetic data. |
| Researcher Affiliation | Academia | Diego Martinez-Taboada Department of Statistics & Data Science Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL Edward H. Kennedy Department of Statistics & Data Science Carnegie Mellon University Pittsburgh, PA 15213, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 DR-MKSD |
| Open Source Code | Yes | Reproducible code for all experiments is provided in the supplementary materials. |
| Open Datasets | Yes | We start by training a dense neural network with layers of size [784, 100, 20, 10] on the MNIST train dataset. |
| Dataset Splits | Yes | For this, we minimize the log entropy loss on 80% of such train data and we store the parameters that minimize the log entropy loss for the remaining validation data (remaining 20% of the MNIST train dataset). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU/CPU models, memory, or specific cloud instance types. |
| Software Dependencies | No | The paper mentions using 'scikit-learn package' for various classifiers (Logistic Regression, Ada Boost, Random Forest) but does not provide specific version numbers for these packages or for Python. |
| Experiment Setup | Yes | Parameter θ was estimated by gradient descent for a number of 1000 steps. (Appendix B.1, B.2) and We estimate the minimizer of gn(θ) by gradient descent. (Section 5) and Figure 3 exhibits the values of gn over a grid {(θ1, θ2) : θ1, θ2 { 5, 4.9, 4.8 . . . , 5}}. (Appendix B.3) and π with Default Logistic Regression from the scikit-learn package with C = 1e5 and max iter = 1000 (Appendix B.1). |