3 papers
cs.LG2026
When to Align, When to Predict: A Phase Diagram for Multimodal Learning
Ilay Kamai, Hugues Van Assel, Aviv Regev +2
Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each…
cs.LG2026
A Geometric View of Counterfactual Behavior: Interaction of Boundary Proximity and Local Support
Ioanna Gemou, Matteo Gamba, Randall Balestriero +1
Counterfactual explanations seek small, semantically meaningful changes to an input that alter a model's prediction, and are widely used to interpret and audit machine learning sys…
cs.LG2025
SAFE: A Novel Approach to AI Weather Evaluation through Stratified Assessments of Forecasts over Earth
Nick Masi, Randall Balestriero
The dominant paradigm in machine learning is to assess model performance based on average loss across all samples in some test set. This amounts to averaging performance geospatial…