Statistical Learning
Distributed Personalized Empirical Risk Minimization
To mitigate the negative effect of data heterogeneity (non-IIDness), two common approaches are clustering and personalization.
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
Rare-events data refer to binary-response data that are highly imbalanced, i.e., the number of zeros
Stopping Bayesian Optimization with Probabilistic Regret Bounds
Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.
Statistical Multicriteria Benchmarking via the GSD-Front
For (3), we relax our proposed test using techniques from robust statistics and imprecise probabilities.
On the Identifiability and Interpretability of Gaussian Process Models
Rather than specifying a model, the GP framework revolves around selecting an appropriate kernel, transforming the problem into one of parameter estimation.
ADGym: Design Choices for Deep Anomaly Detection
Many WSAD methods attribute their improvements to novel network architectures or loss functions, based on the authors' understanding of