Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Normalizing Flows for Knockoff-free Controlled Feature Selection
Authors: Derek Hansen, Brian Manzo, Jeffrey Regier
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,459 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,520 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, FLOWSELECT consistently controls the FDR on both synthetic and semi-synthetic benchmarks, whereas competing knockoff-based approaches do not. FLOWSELECT also demonstrates greater power on these benchmarks. Additionally, FLOWSELECT correctly infers the genetic variants associated with specific soybean traits from GWAS data. |
| Researcher Affiliation | Academia | Derek Hansen Department of Statistics University of Michigan EMAIL Brian Manzo Department of Statistics University of Michigan EMAIL Jeffrey Regier Department of Statistics University of Michigan EMAIL |
| Pseudocode | Yes | Algorithm 1 Step 2 of the FLOWSELECT procedure for drawing K null features Xi,j|Xi, j for feature j at observation i. |
| Open Source Code | Yes | Software to reproduce our experiments is available at https://github.com/dereklhansen/flowselect. |
| Open Datasets | Yes | We use single-cell RNA sequencing (sc RNA-seq) data from 10x Genomics (10x Genomics, 2017)... We tested FLOWSELECT on a dataset from the Soy NAM project (Song et al., 2017) |
| Dataset Splits | Yes | For each model, we use 90% of the data for training to generate null features and the remaining 10% for calculating the feature statistics. |
| Hardware Specification | No | The paper mentions running experiments "using a single GPU" in Section 5.4, but it does not specify the model or any other detailed hardware specifications. |
| Software Dependencies | No | The paper mentions various software components and methods used (e.g., LASSO, random forest regressor, normalizing flows), but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper describes the general experimental setup, such as data splits (90% training, 10% for statistics) and the types of models used (LASSO, random forest, neural network architecture), but it does not provide specific hyperparameters like learning rates, batch sizes, or explicit numbers of MCMC samples in the main text. It defers "Additional details of training and architecture" to Appendices E. |