South America
Automatically Learning Hybrid Digital Twins of Dynamical Systems Samuel Holt
However, existing approaches to DTs often struggle to generalize to unseen conditions in data-scarce settings, a crucial requirement for such models. To address these limitations, our work begins by establishing the essential desiderata for effective DTs.
Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification
Uncertainty Quantification (UQ) is crucial for reliable image segmentation.
Promises and Pitfalls of Threshold-based Auto-labeling
TBAL is emerging as a widely-used solution in practice.
Optimal cross-learning for contextual bandits with unknown context distributions
We consider the problem of designing contextual bandit algorithms in the "cross-learning" setting of Balseiro et al., where the learner observes the loss for the action
Optimal cross-learning for contextual bandits with unknown context distributions
We consider the problem of designing contextual bandit algorithms in the "cross-learning" setting of Balseiro et al., where the learner observes the loss for the action
Faster Differentially Private Top-k Selection: A Joint Exponential Mechanism with Pruning Hao WU
Their algorithm has a time and space complexity of O ( dk) .
