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Found 3 papers, 1 papers with code
Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components
We then study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements.
(Nearly) Optimal Private Linear Regression via Adaptive Clipping
Compared to existing $(\epsilon, \delta)$-DP techniques which have sub-optimal error bounds, DP-AMBSSGD is able to provide nearly optimal error bounds in terms of key parameters like dimensionality $d$, number of points $N$, and the standard deviation $\sigma$ of the noise in observations.
CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE
Out of the three subtasks, this paper reports the system description of subtask A and subtask B.
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