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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Generator Assisted Mixture of Experts for Feature Acquisition in Batch
Authors: Vedang Asgaonkar, Aditya Jain , Abir De
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with four datasets show that our approach outperforms these methods in terms of trade-off between accuracy and feature acquisition cost. |
| Researcher Affiliation | Academia | Indian Institute of Technology Bombay EMAIL |
| Pseudocode | Yes | Algorithm 1: Training. Algorithm 2: Inference. |
| Open Source Code | Yes | Our code is in https://github.com/Vedang Asgaonkar/genex |
| Open Datasets | Yes | We experiment with four datasets for the classification task; DP (disease prediction), MNIST, CIFAR100 and Tiny Imagenet (TI). Details are provided in the extended version (Asgaonkar, Jain, and De 2023). (Extended version of current paper). ar Xiv preprint arxiv:2312.12574. |
| Dataset Splits | Yes | We split the entire dataset in 70% training, 10% validation and 20% test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like "Python", "PyTorch", "β-VAE", "Wide Resnet", and "Efficient Net" but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | We set the number of buckets B = 8, 8, 4, 4 for DP, MNIST, CIFAR100 and Tiny Imagenet using cross validation. given a budget qmax for maximum number of oracle queries for each instance. where λ is a hyperparameter. |