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..
Statistical-Computational Tradeoff in Single Index Models
Authors: Lingxiao Wang, Zhuoran Yang, Zhaoran Wang
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the statistical-computational tradeoffs in a high dimensional single index model Y = f(X β ) + ϵ, where f is unknown, X is a Gaussian vector and β is s-sparse with unit norm. ... Using the statistical query model to characterize the computational cost of an algorithm, we show that when Cov(Y, X β ) = 0 and Cov(Y, (X β )2) > 0, no computationally tractable algorithms can achieve the information-theoretic limit of the minimax risk. This implies that one must pay an extra computational cost for the nonlinearity involved in the model. |
| Researcher Affiliation | Academia | Northwestern University; EMAIL Princeton University; EMAIL Northwestern University; EMAIL |
| Pseudocode | No | The paper does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with datasets, thus no training data information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with datasets, thus no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation details or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. |