Statistical Learning
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
These jump from their maximum possible value to zero, at well denullned signal-to-noise thresholds whose asymptotic values we determine exactly.
All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation
These jump from their maximum possible value to zero, at well denullned signal-to-noise thresholds whose asymptotic values we determine exactly.
Appendices T able of Contents
Thm. 4.4 assumes, without loss of generality, that all covariates are standardized to have mean
Probable Domain Generalization via Quantile Risk Minimization Cian Eastwood 1,2 Alexander Robey 3 Shashank Singh 1 Julius von Kügelgen
To achieve this, DG is commonly formulated as an average-or worst-case problem over the set of possible domains.