On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
Authors: Prateek Jaiswal, Harsha Honnappa, Vinayak Rao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our theoretical findings in parametric and nonparametric settings with the help of three examples. We also present some simulation results with the single product (1-d) newsvendor problem, which is summarized here in Figure 1. |
| Researcher Affiliation | Academia | Prateek Jaiswal Department of Statistics Texas A&M University College Station, TX 77843 jaiswalp@stat.tamu.edu; Harsha Honnappa School of Industrial Engineering Purdue University West Lafayette, IN, 47906 honnappa@purdue.edu; Vinayak A. Rao Department of Statistics Purdue University West Lafayette, IN, 47907 varao@purdue.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Methods are described in prose and mathematical formulations. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | For the single-product newsvendor and Gaussian process classification problems, data is described as 'i.i.d random samples drawn from P0' or 'generated from the model', indicating simulated data, not a publicly available dataset. For the eight-schools model, it mentions 'We consider the eight-schools problem [33, 15]' which is a well-known problem setting, but it does not provide concrete access information (link, DOI, or specific citation for the dataset itself) for public availability. |
| Dataset Splits | No | The paper states for the eight-schools model: 'due to small size of the dataset in this example, the ERR is evaluated on the training data itself.' This indicates no separate validation split for training or evaluation. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names along with their version numbers that are necessary for replicating the experiments. |
| Experiment Setup | Yes | For the single-product newsvendor model: 'We fix θ0 = 0.1, b = 1, h = 5, α = 1, and β = 4.1. We run RSVB algorithm with γ {0( naive ), 1, 2, 4.5, 5, 6} and repeat the experiment over 100 sample paths.' For the eight-schools model: 'We modified the experiments in [15] by introducing γ = {5, 2.5, 1, 0.5} = {0.2 1, 0.4 1, 1 1, 2 1}.' |