Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Authors: Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the theoretical properties of the proposed quasi-posterior, and demonstrate through empirical evaluation the competitive performance of our method. [...] 6 Experiments |
| Researcher Affiliation | Collaboration | 1 Dept. of Comp. Sci. and Tech., BNRist Center, State Key Lab for Intell. Tech. & Sys., Institute for AI, Tsinghua-Bosch Joint Center for ML, Tsinghua University 2 Department of Computer Science, UT Austin |
| Pseudocode | No | The paper describes the algorithm steps in text (e.g., 'The algorithm has the form of stochastic gradient descent-ascent') but does not provide pseudocode or a clearly labeled algorithm block in the main text. |
| Open Source Code | Yes | Code to reproduce the experiments is available at https://github.com/meta-inf/qbdiv. |
| Open Datasets | Yes | We now turn to the more challenging demand simulation first proposed by [4]. |
| Dataset Splits | No | The paper mentions 'cross validation' for hyperparameter selection but does not specify explicit percentages, sample counts, or specific pre-defined splits for training, validation, and test sets in the main text. |
| Hardware Specification | No | The paper mentions running experiments on 'CPU' and 'GPU' (Table 1), and 'a single accelerator' (Table 1 caption), but no specific models, brands, or detailed specifications for the hardware used are provided. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states 'Hyperparameter for the kernelized IV methods are selected by cross validation...see Appendix D.1. For kernels we choose the RBF and Matérn kernels...See Appendix D.3 for the detailed setup.' but does not list concrete hyperparameter values or training configurations in the main text. |