Notice: 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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Feature Selection in the Contrastive Analysis Setting
Authors: Ethan Weinberger, Ian Covert, Su-In Lee
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We motivate our approach with a novel information-theoretic analysis of representation learning in the CA setting, and we empirically validate CFS on a semi-synthetic dataset and four real-world biomedical datasets. |
| Researcher Affiliation | Academia | Ethan Weinberger Paul G. Allen School of Computer Science University of Washington Seattle, WA 98195 EMAIL Ian C. Covert Department of Computer Science Stanford University Stanford, CA 94305 EMAIL Su-In Lee Paul G. Allen School of Computer Science University of Washington Seattle, WA 98195 EMAIL |
| Pseudocode | No | The paper describes the proposed method (CFS) using text descriptions and mathematical equations, accompanied by a diagram (Figure 2), but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | An open-source implementation of our method is available at https://github.com/suinleelab/CFS. |
| Open Datasets | Yes | We validate our approach empirically through extensive experiments on a semi-synthetic dataset introduced in prior work as well as four real-world biomedical datasets... Raw data was downloaded from https://archive.ics.uci.edu/ml/machine-learning-databases/00342/. |
| Dataset Splits | Yes | For all experiments we divided our data using an 80-20 train-test split, and we report the mean standard error over five random seeds for each method. |
| Hardware Specification | Yes | All experiments were peformed on a system running Cent OS 7.9.2009 equipped with an NVIDIA RTX 2080 TI GPU with CUDA 11.7. |
| Software Dependencies | Yes | CFS models were implemented using Py Torch [50] (version 1.13) with the Py Torch Lightning API4...equipped with an NVIDIA RTX 2080 TI GPU with CUDA 11.7. |
| Experiment Setup | Yes | For all CFS variants we let our reconstruction function f be a multilayer perceptron with two hidden layers of size 512 with Re LU activation functions...All CFS models were trained using the Py Torch implementation of the Adam [51] optimizer with default hyperparameters. Batch sizes of 128 were used for all experiments. |