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..
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Authors: Ahmed Alaa, Mihaela Van Der Schaar
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that DJ performs competitively compared to existing Bayesian and non-Bayesian regression baselines. |
| Researcher Affiliation | Academia | Ahmed M. Alaa 1 Mihaela van der Schaar 1 2 1UCLA 2Cambridge University. |
| Pseudocode | Yes | Algorithm 1 The Discriminative Jackknife |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | on 4 UCI benchmark datasets for regression: yacht hydrodynamics (Yacht), Boston housing (Housing), energy efficiency (Energy) and naval propulsion (Naval) (Dua & Graff, 2017). and reference Dua, D. and Graff, C. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | No | The paper mentions '80% of the data for training and 20% for testing' but does not specify a separate validation split or explicit cross-validation setup details in the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer with default settings' but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we fit a 2-layer feed-forward neural network with 100 hidden units and compute the DJ confidence intervals using the post-hoc procedure in Algorithm 1. and We use a 2-layer neural network model with 100 hidden units, Tanh activation functions, MSE loss, and a single set of learning hyper-parameters for all baselines (1000 epochs with 100 samples per minibatch, and an Adam optimizer with default settings). |