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Found 8 papers, 2 papers with code
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning
A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks.
Bias Challenges in Counterfactual Data Augmentation
Deep learning models tend not to be out-of-distribution robust primarily due to their reliance on spurious features to solve the task.
Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks
Generalizing from observed to new related environments (out-of-distribution) is central to the reliability of classifiers.
Neural Networks for Learning Counterfactual G-Invariances from Single Environments
In order to endow neural networks with the ability to extrapolate over group transformations, we introduce a learning framework counterfactually-guided by the learning hypothesis that any group invariance to (known) transformation groups is mandatory even without evidence, unless the learner deems it inconsistent with the training data.
Neural Network Extrapolations with G-invariances from a Single Environment
In order to endow neural networks with the ability to extrapolate over group transformations, we introduce a learning framework guided by a new learning hypothesis: Any invariance to transformation groups is mandatory even without evidence, unless the learner deems it inconsistent with the training data.
Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms
Our mechanism injects decoy deletions, hence creating a two-player minmax game between an adversary that seeks to classify damaging content among the deleted posts and a challenger that employs decoy deletions to masquerade real damaging deletions.
Deep Lifetime Clustering
The goal of lifetime clustering is to develop an inductive model that maps subjects into $K$ clusters according to their underlying (unobserved) lifetime distribution.
A Deep Learning Approach for Survival Clustering without End-of-life Signals
The goal of survival clustering is to map subjects (e. g., users in a social network, patients in a medical study) to $K$ clusters ranging from low-risk to high-risk.
