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 [1].
Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
Authors: Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are provided to corroborate the theoretical results. |
| Researcher Affiliation | Academia | Xuxing Chen Department of Mathematics University of California, Davis EMAIL Abhishek Roy Halıcıo glu Data Science Institute University of California, San Diego EMAIL Yifan Hu College of Management, EPFL Department of Computer Science, ETH Zurich EMAIL Krishnakumar Balasubramanian Department of Statistics University of California, Davis EMAIL |
| Pseudocode | Yes | Algorithm 1 Two-sample One-stage Stochastic Gradient-IVa R (TOSG-IVa R) Input: of iterations T, stepsizes {αt}T t=1, initial iterate θ1. |
| Open Source Code | Yes | We also provide the code in the main supplemental material. |
| Open Datasets | Yes | We further conduct experiments on real-world datasets provided in [AE96] and [Rya12]. |
| Dataset Splits | No | The paper describes training data generation and test samples but does not explicitly mention or describe a separate validation split for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | All experiments in Section 4 were conducted on a computer with an 11th Intel(R) Core(TM) i711370H CPU. |
| Software Dependencies | No | The paper mentions the CPU used for experiments but does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | We set (dx, dz) {(4, 8), (8, 16)}, c {0.1, 1.0}, and ϕ(s) {s, s2}. [...] set αt α = log T /µT [...] αt = Cαt 1+ι/2 and βt = Cβt 1+ι/2 |