Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits
Authors: Markus Bläser
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As our main result, we prove that this question has a negative answer. There are strongly Rayleigh distributions that cannot be represented by polynomial-sized probabilistic generating circuits, assuming a widely accepted complexity theoretic conjecture. |
| Researcher Affiliation | Academia | 1Saarland University, Saarland Informatics Campus, Saarbrücken, Germany. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It is a theoretical paper focused on mathematical proofs. |
| Open Source Code | No | The paper does not provide any information about open-source code availability. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or any other purpose. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits or validation procedures. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve any experimental setup details, hyperparameters, or training configurations. |