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].
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization
Authors: Weijia Zhang, Xuanhui Zhang, hanwen deng, Min-Ling Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world datasets demonstrate that our approach significantly outperforms various baselines on instance label prediction and out-of-distribution generalization tasks. In this section, we first evaluate the instance label prediction performances of Causal MIL against MIL baselines... Then, we evaluate the out-of-distribution generalization ability of Causal MIL by comparing with supervised learning algorithms... |
| Researcher Affiliation | Academia | Weijia Zhang1 , Xuanhui Zhang2, Han-Wen Deng1, Min-Ling Zhang1 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2 School of Information Management, Nanjing University, Nanjing 210023, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No section or figure explicitly labeled 'Pseudocode' or 'Algorithm' was found in the paper. |
| Open Source Code | Yes | The implementation code is publicly available at https://github. com/Weijia Zhang24/Causal MIL. |
| Open Datasets | Yes | Our quantitative evaluation first utilizes 30 multi-instance classification datasets generated from the MNIST [19], Fashion MNIST [38], and Kuzushiji MNIST [7] datasets... We also evaluate Causal MIL on a hematoxylin and eosin (H&E) stained Colon Cancer histopathology task [32]. |
| Dataset Splits | Yes | Experiments are repeated for 5 times and the average metric standard deviation of 5-fold cross validations are reported. |
| Hardware Specification | Yes | We implemented Causal MIL using Py Torch and conducted most of the experiments with a single NVIDIA RTX3090 GPU. |
| Software Dependencies | No | We implemented Causal MIL using Py Torch and conducted most of the experiments with a single NVIDIA RTX3090 GPU. |
| Experiment Setup | Yes | Detailed settings, parameters, and data for reproducing the results are provided in the Appendices. |