The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics

Authors: Afonso S Bandeira, Ahmed El Alaoui, Samuel Hopkins, Tselil Schramm, Alexander S Wein, Ilias Zadik

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical Our work is purely theoretical and has no societal impacts.
Researcher Affiliation Academia Afonso S. Bandeira Department of Mathematics ETH Zürich bandeira@math.ethz.ch Ahmed El Alaoui Department of Statistics and Data Science Cornell University ae333@cornell.edu Samuel B. Hopkins MIT EECS Cambridge, MA samhop@mit.edu Tselil Schramm Department of Statistics Stanford University tselil@stanford.edu Alexander S. Wein Department of Mathematics University of California, Davis aswein@ucdavis.edu Ilias Zadik Department of Mathematics MIT izadik@mit.edu
Pseudocode No The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical proofs and mathematical relationships.
Open Source Code No The paper states 'Our work is purely theoretical and has no societal impacts.' It does not provide any links to open-source code or explicitly state that code for the methodology is available.
Open Datasets No The paper is theoretical and discusses models and mathematical concepts, not the use of specific datasets for training. It does not mention any publicly available or open datasets used in an experimental context.
Dataset Splits No The paper is theoretical and does not describe experimental validation on datasets, therefore no training/validation/test splits are provided.
Hardware Specification No The paper is purely theoretical and does not describe any computational experiments that would require specific hardware. All related checklist items are marked 'N/A'.
Software Dependencies No The paper is purely theoretical and does not list any software dependencies with specific version numbers. All related checklist items are marked 'N/A'.
Experiment Setup No The paper is theoretical and does not describe an experimental setup, including hyperparameters or system-level training settings. All related checklist items are marked 'N/A'.