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..
Diffusion Posterior Sampling is Computationally Intractable
Authors: Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price, Zhiyang Xun
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we show that posterior sampling is computationally intractable: under the most basic assumption in cryptography that one-way functions exist there are instances for which every algorithm takes superpolynomial time, even though unconditional sampling is provably fast. We also show that the exponential-time rejection sampling algorithm is essentially optimal under the stronger plausible assumption that there are one-way functions that take exponential time to invert. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Texas at Austin 2Department of Electrical Engineering and Computer Science, University of California, Berkeley. |
| Pseudocode | Yes | Algorithm 1 Rejection Sampling Algorithm |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use any datasets for training. Therefore, it does not provide access information for a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with dataset splits. It does not provide any information about training/test/validation splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |