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..
Conformal Prediction as Bayesian Quadrature
Authors: Jake C. Snell, Thomas L. Griffiths
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on both synthetic data and calibration data collected from MS-COCO (Lin et al., 2014). For each data setting, we randomly generate M = 10,000 data splits. Each method is used to select λ with the goal of controlling the risk such that R(θ, λ) α for unknown θ. We compare algorithms on the basis of both the relative frequency of incurring risk greater than α and the prediction set size of the chosen λ. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Princeton University 2Department of Psychology, Princeton University. Correspondence to: Jake C. Snell <EMAIL>. |
| Pseudocode | No | The paper describes methods in prose and does not contain structured pseudocode or algorithm blocks in the provided text. |
| Open Source Code | Yes | Code for our experiments is publicly available on Github.4 |
| Open Datasets | Yes | We also compare methods on controlling the false negative rate of multilabel classification on the MS-COCO dataset (Lin et al., 2014). |
| Dataset Splits | Yes | For each data setting, we randomly generate M = 10,000 data splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For each data setting, we randomly generate M = 10,000 data splits. Each method is used to select λ with the goal of controlling the risk such that R(θ, λ) α for unknown θ. We set n = 10, K = 4, and α = 0.4. Monte Carlo simulation of Dirichlet random variates with 1000 samples. |