Integrated Latent Heterogeneity and Invariance Learning in Kernel Space
Authors: Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, experiments on both synthetic and real-world data validate the superiority of Ker HRM in terms of good out-of-distribution generalization performance. |
| Researcher Affiliation | Academia | 1 Department of Computer Science & Technology, Tsinghua University, Beijing, China 2Department of Computer Science, National University of Singapore, Singapore 3School of Economics and Management, Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Kernelized Heterogeneous Risk Minimization (Ker HRM) Algorithm |
| Open Source Code | Yes | Our code is available at https://github.com/LJSthu/Kernelized-HRM. |
| Open Datasets | Yes | For the Colored MNIST dataset, the paper references '[1] MartÃn Arjovsky et al., Invariant risk minimization, 2019'. For the house sales prices dataset, it states 'Kaggle) of house sales prices from King County, USA3' with a footnote link 'https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data'. |
| Dataset Splits | Yes | In training, we set d = 5 and generate 2000 data points, where 50% points are from environment e1 with r1 = 0.9 and the other from environment e2 with r2... For IRM, we sample 1000 data from the two training environments respectively and select the hyper-parameters which maximize the minimum accuracy of two validation environments. |
| Hardware Specification | No | The paper describes the methods and experiments but does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming language versions, library versions, or framework versions. |
| Experiment Setup | Yes | For all experiments, we use a two-layer MLP with 1024 hidden units... For our method, we set the cluster number K = 2... For IRM, we sample 1000 data from the two training environments respectively and select the hyper-parameters which maximize the minimum accuracy of two validation environments. |