10 citations · 31 across the 12 of their papers we have counts for
12 papers
Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution
Shravan Chaudhari, Yoav Wald, Suchi Saria
As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts. Such shifts can take many forms: new cl…
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
Hsing-Huan Chung, Shravan Chaudhari, Xing Han +3
Dynamic graph learning is essential for applications involving temporal networks and requires effective modeling of temporal relationships. Seminal attention-based models like TGAT…
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
Gina Wong, Joshua Gleason, Rama Chellappa +2
Learning models whose predictions are invariant under multiple environments is a promising approach for out-of-distribution generalization. Such models are trained to extract featu…
Novel Node Category Detection Under Subpopulation Shift
Hsing-Huan Chung, Shravan Chaudhari, Yoav Wald +2
In real-world graph data, distribution shifts can manifest in various ways, such as the emergence of new categories and changes in the relative proportions of existing categories.…
Data Augmentations for Improved (Large) Language Model Generalization
Amir Feder, Yoav Wald, Claudia Shi +2
The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthc…
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy
Aahlad Puli, Lily Zhang, Yoav Wald +1
Common explanations for shortcut learning assume that the shortcut improves prediction under the training distribution but not in the test distribution. Thus, models trained via th…
Malign Overfitting: Interpolation Can Provably Preclude Invariance
Yoav Wald, Gal Yona, Uri Shalit +1
Learned classifiers should often possess certain invariance properties meant to encourage fairness, robustness, or out-of-distribution generalization. However, multiple recent work…
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
Aahlad Puli, Nitish Joshi, Yoav Wald +2
In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics. Features…
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Amir Feder, Guy Horowitz, Yoav Wald +2
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recomm…
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom +8
Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize…
On Calibration and Out-of-domain Generalization
Yoav Wald, Amir Feder, Daniel Greenfeld +1
Out-of-domain (OOD) generalization is a significant challenge for machine learning models. Many techniques have been proposed to overcome this challenge, often focused on learning…
Robust Conditional Probabilities
Yoav Wald, Amir Globerson
Conditional probabilities are a core concept in machine learning. For example, optimal prediction of a label given an input corresponds to maximizing the conditional probab…