Trustworthy Monte Carlo
Authors: Juha Harviainen, Mikko Koivisto, Petteri Kaski
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 juha.harviainen@helsinki.fi Petteri Kaski Aalto University petteri.kaski@aalto.fi Mikko Koivisto University of Helsinki mikko.koivisto@helsinki.fi |
| 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. |