Total Variation Floodgate for Variable Importance Inference in Classification
Authors: Wenshuo Wang, Lucas Janson, Lihua Lei, Aaditya Ramdas
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our algorithms with simulations and a case study in conjoint analysis on the US general election. |
| Researcher Affiliation | Academia | 1Department of Statistics, Harvard University, Cambridge, MA, USA 2Stanford Graduate School of Business, Stanford University, Stanford, CA, USA 3Departments of Statistics and Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA. |
| Pseudocode | Yes | Algorithm 1 Total variation floodgate. ... Algorithm 2 Cross-validated total variation floodgate. ... Algorithm 3 Hierarchically weighted TV floodgate. |
| Open Source Code | Yes | The code to implement ETV floodgate and replicate all experiments is available at https://github.com/ wenshuow/etv_floodgate. |
| Open Datasets | Yes | We analyze the election data in Ono & Burden (2019), which is under the CC0 license. |
| Dataset Splits | Yes | We set p = 4 or 10, β = (0, 1, 2, 3) for p = 4 and β = (0, 0, 0, 0, 1, 2, 3, 4, 5, 6) for p = 10, n = 100p, and apply 10-fold cross validation in Algorithm 2. |
| Hardware Specification | No | The paper mentions that experiments run 'on a single CPU' but provides no further specific details about the CPU model, speed, or other hardware components. |
| Software Dependencies | No | The paper mentions using Hier Net models and provides a GitHub link to the code. However, it does not list specific software dependencies (e.g., programming languages, libraries, or frameworks) with their version numbers within the text. |
| Experiment Setup | Yes | We set p = 4 or 10... n = 100p, and apply 10-fold cross validation in Algorithm 2. ... We apply Algorithm 2 with k = 10 and J = 100. The classifier family f is chosen to be the model-based f in equation (5), where the models are Hier Net (Bien et al., 2013)... where pθ1(y | x, z) is a Hier Net model with a fixed penalty parameter... |