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..
Fundamentals of Task-Agnostic Data Valuation
Authors: Mohammad Mohammadi Amiri, Frederic Berdoz, Ramesh Raskar
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We will show through extensive experiments on real tabular and image datasets that the proposed estimates capture the diversity and relevance of the seller s data for the buyer. Experiments We evaluate our estimates for diversity and relevance using real datasets, namely Adult (Kohavi), MNIST (Le Cun et al.), fashion-MNIST (Xiao et al.), Cifar-10 (Krizhevsky) and Fair Face (Karkkainen et al.). |
| Researcher Affiliation | Academia | Mohammad Mohammadi Amiri1, Fr ed eric Berdoz2, Ramesh Raskar1 1MIT, Media Lab, 75 Amherst St, Cambridge, MA 02139, USA 2EPFL, Lausanne, Switzerland |
| Pseudocode | No | The paper describes the proposed method in prose and mathematical formulas, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to a code repository. |
| Open Datasets | Yes | We evaluate our estimates for diversity and relevance using real datasets, namely Adult (Kohavi), MNIST (Le Cun et al.), fashion-MNIST (Xiao et al.), Cifar-10 (Krizhevsky) and Fair Face (Karkkainen et al.). |
| Dataset Splits | No | The paper does not specify explicit train/validation/test splits, percentages, or sample counts for the datasets used in its experiments. It mentions |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using "VGG16 model pre-trained on the Image Net dataset" but does not specify any software names with version numbers (e.g., Python, TensorFlow, PyTorch versions) that would be needed for reproducibility. |
| Experiment Setup | No | The paper describes the conceptual setup for calculating diversity and relevance metrics (e.g., using principal components with eigenvalues > 10^-2), but it does not provide specific hyperparameter values (like learning rate, batch size, epochs) or detailed system-level training settings as would be typical for a machine learning model training experiment. |