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..
Trustworthy Monte Carlo
Authors: Juha Harviainen, Mikko Koivisto, Petteri Kaski
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Although this work is theoretical and leaves experimentation for future works, we include a proof-of-concept Mathematica [11] implementation for the example of Section 3.4 in the supplement. |
| Researcher Affiliation | Academia | Juha Harviainen University of Helsinki EMAIL Petteri Kaski Aalto University EMAIL Mikko Koivisto University of Helsinki EMAIL |
| Pseudocode | Yes | Algorithm V V1 Send the prover the problem instance and the points ξ1, ξ2, . . . , ξe. V2 Receive from the prover a proof, i.e., a claimed value yk of p(ξk) for each k = 1, 2, . . . , e. V3 Find the coefficients of p(x) = PD k=0 pkxk such that p(ξk) = yk for all k = 1, 2, . . . , e. V4 Draw a random point ξ0 F and evaluate p(ξ0) and p(ξ0). V5 If p(ξ0) = p(ξ0), then accept the proof and consume the values yk; otherwise reject the proof. |
| Open Source Code | Yes | Although this work is theoretical and leaves experimentation for future works, we include a proof-of-concept Mathematica [11] implementation for the example of Section 3.4 in the supplement. |
| Open Datasets | No | The paper describes theoretical methods and does not detail empirical experiments involving specific datasets, their public availability, or training procedures. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications for running experiments are provided. |
| Software Dependencies | Yes | Wolfram Research, Inc. Mathematica, Version 13.0.0. Champaign, IL, 2021. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or their setup, including hyperparameters or system-level training settings. |