reiinakano / invariant-risk-minimization
Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893
☆85Updated 5 years ago
Alternatives and similar repositories for invariant-risk-minimization:
Users that are interested in invariant-risk-minimization are comparing it to the libraries listed below
- ☆64Updated 4 years ago
- This repo provides code used in the paper "Predicting with High Correlation Features" (https://arxiv.org/abs/1910.00164):☆54Updated 5 years ago
- ☆87Updated 7 months ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 3 years ago
- ☆54Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- ☆89Updated 3 years ago
- A way to achieve uniform confidence far away from the training data.☆37Updated 3 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).☆48Updated 3 years ago
- Computing various measures and generalization bounds on convolutional and fully connected networks☆35Updated 6 years ago
- Gradients as Features for Deep Representation Learning☆43Updated 4 years ago
- ☆58Updated last year
- ☆121Updated 9 months ago
- ☆36Updated 3 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- Official implementation of paper Gradient Matching for Domain Generalization☆119Updated 3 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 4 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- ☆34Updated 3 years ago
- Repository for theory and methods for Out-of-Distribution (OoD) generalization☆63Updated 2 years ago
- Regularized Learning under label shifts☆18Updated 5 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆70Updated 9 months ago
- Code for Invariant Rep. Without Adversaries (NIPS 2018)☆35Updated 5 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Implementation of Bayesian Gradient Descent☆37Updated last year
- An official PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (NeurIPS 2019) by Risto Vuorio*…☆139Updated 5 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆140Updated 4 years ago
- PyTorch Implementation of Neural Statistician☆60Updated 3 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆55Updated 3 years ago