Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Authors: Ahmed Alaa, Mihaela Van Der Schaar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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). |