Provable Domain Generalization via Invariant-Feature Subspace Recovery
Authors: Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our ISRs can obtain superior performance compared with IRM on synthetic benchmarks. In addition, on three real-world image and text datasets, we show that both ISRs can be used as simple yet effective post-processing methods to improve the worst-case accuracy of (pre-)trained models against spurious correlations and group shifts. ... We conduct experiments on both synthetic and real datasets to examine our proposed algorithms. |
| Researcher Affiliation | Academia | Haoxiang Wang 1 Haozhe Si 1 Bo Li 1 Han Zhao 1 1University of Illinois at Urbana-Champaign, Urbana, IL, USA. Correspondence to: Haoxiang Wang <hwang264@illinois.edu>. |
| Pseudocode | Yes | Algorithm 1 ISR-Mean Algorithm 2 ISR-Cov |
| Open Source Code | Yes | The code is released at https: //github.com/Haoxiang-Wang/ISR. |
| Open Datasets | Yes | Waterbirds (Sagawa et al., 2019): This is a image dataset built from the CUB (Wah et al., 2011) and Places (Zhou et al., 2017) datasets. ... Celeb A (Liu et al., 2015): This is a celebrity face dataset... ... Multi NLI (Williams et al., 2017): This is a text dataset for natural language inference. |
| Dataset Splits | Yes | We choose the hyper-parameters that minimize the mean error over the validation split of all environments. ... early stop models at the epoch with the highest worst-group validation accuracy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "Adam optimizer" and "logistic regression solver provided in scikit-learn", but does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | for 10K full-batch Adam ... iterations. ... We choose the hyper-parameters that minimize the mean error over the validation split of all environments. ... early stop models at the epoch with the highest worst-group validation accuracy. ... Adopting the same hyperparameter as that of Table 1 |