A Model-free Closeness-of-influence Test for Features in Supervised Learning
Authors: Mohammad Mehrabi, Ryan A. Rossi
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our findings through extensive numerical simulations, specifically we adopt the datamodel framework (Ilyas, et al., 2022) for CIFAR-10 dataset to identify pairs of training samples with different influence on the trained model via optional black box training mechanisms. |
| Researcher Affiliation | Collaboration | 1Department of Data Sciences and Operations, University of Southern California, Los Angeles, USA 2Adobe, San Jose, USA. |
| Pseudocode | Yes | Algorithm 1 Test statistic for hypothesis testing 1 |
| Open Source Code | No | The paper provides a link to |
| Open Datasets | Yes | We consider the CIFAR-10 dataset (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper mentions training and testing samples but does not explicitly describe a validation split for model training or evaluation. It mentions |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. It vaguely mentions |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | We run our method with the score function T(x, y) = |y bθTx| with bθ N(0, Id). The estimate bθ is fixed across all 45 tests. We suppose that we have access to 1000 data points, and we consider three different significance levels α = 0.1, 0.15, and 0.2. |