Explaining Groups of Points in Low-Dimensional Representations
Authors: Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that TGT is able to identify explanations that accurately explain the model while being relatively sparse, and that these explanations match real patterns in the data. We demonstrate the usefulness of TGT with a series of experiments on synthetic, UCI, and single-cell RNA datasets. In our experiments, we measure the effectiveness of explanations using correctness and coverage, with sparsity as a proxy metric for interpretability, and we compare the patterns the explanations find to those we expect to be in the data. |
| Researcher Affiliation | Collaboration | Gregory Plumb 1 Jonathan Terhorst 2 Sriram Sankararaman 3 Ameet Talwalkar 1 4 1Carnegie Mellon University 2University of Michigan 3University of California, Los Angeles 4Determined AI. |
| Pseudocode | Yes | Algorithm 1 TGT: Calculating GCEs with a Reference Group. Algorithm 2 How to construct any explanation between an arbitrary pair of groups, ti!j... Algorithm 3 How to update the basis explanations... |
| Open Source Code | Yes | Code for all algorithms and experiments is available at https://github.com/GDPlumb/ELDR |
| Open Datasets | Yes | We use the model from (Ding et al., 2018) on the UCI Iris, Boston Housing, and Heart Disease datasets (Dua & Graff, 2017) and a single-cell RNA dataset (Shekhar et al., 2016) |
| Dataset Splits | No | The paper describes using datasets and training models but does not provide specific details on train, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an 'autodifferentiation software' but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | Algorithm 1 TGT... Input: Model: r Group Means: xi (feature space) and ri (representation space)... l1 Regularization Weight: λ Learning Rate: Initialize: δ1, . . . , δl 1 to vectors of 0... |