Releasing seemingly innocuous functions of a data set can easily compromise the privacy of individuals, whether the functions are simple counts [35]orcomplexmachine learning models like deep neural networks [52,30].

The important steps to formulate our approximate SW are summarized as follows.

Our VB-LoRA achieves higher scores with significantly smaller number ofstoredparameters.

Partial-label learning(PLL) is a multi-class classification problem, where each training example isassociated withasetofcandidate labels.

Ouranalysis culminates inshowing thatthere exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of eO(1/3 n) in L1 error.