Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
Authors: Xuxing Chen, Abhishek Roy, Yifan Hu, Krishnakumar Balasubramanian
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 xuxchen@ucdavis.edu Abhishek Roy Halıcıo glu Data Science Institute University of California, San Diego a2roy@ucsd.edu Yifan Hu College of Management, EPFL Department of Computer Science, ETH Zurich yifan.hu@epfl.ch Krishnakumar Balasubramanian Department of Statistics University of California, Davis kbala@ucdavis.edu |
| 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 |