Statistical Learning
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning Yiqin Lv Qi Wang
Accordingly, extensive evaluations demonstrate the significance of our proposal and its scalability to multimodal large models in boosting robustness.
Provable Posterior Sampling with Denoising Oracles via Tilted Transport
From a Bayesian perspective, they offer a realistic modeling of data priors and facilitate solving inverse problems through posterior sampling.
Auditing Privacy Mechanisms via Label Inference Attacks
We propose reconstruction advantage measures to audit label privatization mechanisms.
Rotating Features for Object Discovery
In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations.
OptimalRatesforVector-ValuedSpectral RegularizationLearningAlgorithms
We study theoretical properties of a broad class of regularized algorithms with vector-valued output.
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training Y anlai Y ang 1, Matt Jones
The behavior emerges and becomes more robust as the architecture scales up its number of parameters.
Adapting Fairness Interventions to Missing Values
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.
