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..
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
Authors: Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features learned by deep neural networks in two stages. We prove that the DFIV algorithm achieves the minimax optimal learning rate when the target structural function lies in a Besov space. |
| Researcher Affiliation | Academia | 1Department of Mathematical Informatics, University of Tokyo 2Center for Advanced Intelligence Project, RIKEN 3Gatsby Computational Neuroscience Unit, University College London |
| Pseudocode | No | The paper describes the DFIV algorithm in prose and mathematical equations in Section 2.3 but does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using specific datasets. It refers to generic 'm i.i.d. samples D1 = {(xi, zi)}m i=1 from (X, Z) for Stage 1, and n i.i.d. samples D2 = {( yi, zi)}n i=1 from (Y, Z) for Stage 2', which are conceptual samples for theoretical analysis rather than specific public datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using specific datasets, thus there are no mentions of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe the execution of experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe the execution of experiments, therefore no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe the execution of experiments, therefore no details about experimental setup or hyperparameters are provided. |