Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem.
Jia-Jie ZhuWittawat JitkrittumMoritz DiehlBernhard SchölkopfPublished in: CDC (2020)
Keyphrases
- worst case
- laplacian eigenmaps
- upper bound
- lower bound
- average case
- graph embedding
- risk assessment
- risk management
- support vector
- risk measures
- kernel function
- co occurrence
- np hard
- high risk
- approximation algorithms
- kernel methods
- greedy algorithm
- worst case analysis
- risk factors
- nonlinear dimensionality reduction
- vector space
- worst case scenario
- feature space
- kernel pca
- kernel regression
- error bounds
- kernel learning
- component analysis
- information hiding
- multi class
- kernel matrix
- low dimensional
- similarity function
- similarity measure
- convolution kernel