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 [1].
Joint Shapley values: a measure of joint feature importance
Authors: Chris Harris, Richard Pymar, Colin Rowat
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS; 4.1 GAME THEORETICAL; 4.2 THE AI/ML ATTRIBUTION PROBLEM; 4.2.1 SIMULATED DATA; 4.2.2 BOSTON HOUSING DATA; 4.2.3 MOVIE REVIEWS; Table 1: Joint and interaction measures in n = 3 game theory examples; Table 2: Uniform random variables; k = 3; Table 3: Bernoulli(0.5) random variables; k = 3; Table 4: Joint Shapley values for the Boston dataset; Table 5: Examples of local joint Shapley values in the Pang & Lee (2005) movie reviews |
| Researcher Affiliation | Collaboration | Chris Harris Tokyo, Japan Raptor Financial Technologies EMAIL; Richard Pymar Economics, Mathematics & Statistics Birkbeck College University of London, UK EMAIL; Colin Rowat Economics University of Birmingham, UK EMAIL |
| Pseudocode | No | The paper provides mathematical derivations and proofs, but no pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | Our proofs and source code are available in the accompanying supplemental material; all data are taken from the public domain. |
| Open Datasets | Yes | For comparability, we follow Dhamdhere et al. (2020) by training a random forest on the Boston housing dataset (Harrison & Rubinfeld, 1978)...; We train a fully connected neural network (two hidden layers, 16 units per layer, Re LU activations) on the binary movie review classifications in Pang & Lee (2005).; all data are taken from the public domain. |
| Dataset Splits | No | The paper mentions a "test block" for the movie reviews but does not explicitly specify validation splits or ratios for any of the datasets used in its experiments. |
| Hardware Specification | Yes | All experiments are run on a single Intel(R) Core(TM) i7-6820HQ CPU. |
| Software Dependencies | No | The paper states "Training details and tuning parameters are provided in the accompanying code" and mentions training a "fully connected neural network" but does not specify software or library versions used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | Training details and tuning parameters are provided in the accompanying code. We train a fully connected neural network (two hidden layers, 16 units per layer, Re LU activations). |