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Found 6 papers, 1 papers with code
Privately Estimating a Gaussian: Efficient, Robust and Optimal
We prove a new lower bound on differentially private covariance estimation to show that the dependence on the condition number $\kappa$ in the above sample bound is also tight.
Differentially Private Simple Linear Regression
Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data.
The Cost of a Reductions Approach to Private Fair Optimization
Through the lens of information-theoretic reductions, we examine a reductions approach to fair optimization and learning where a black-box optimizer is used to learn a fair model for classification or regression.
Learning to Prune: Speeding up Repeated Computations
We present an algorithm that learns to maximally prune the search space on repeated computations, thereby reducing runtime while provably outputting the correct solution each period with high probability.
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity.
Learning Certifiably Optimal Rule Lists for Categorical Data
We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space.
