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Found 7 papers, 0 papers with code
The Performance Analysis of Generalized Margin Maximizers on Separable Data
The performance of hard-margin SVM has been recently analyzed in~\cite{montanari2019generalization, deng2019model}.
Stochastic Mirror Descent in Average Ensemble Models
The stochastic mirror descent (SMD) algorithm is a general class of training algorithms, which includes the celebrated stochastic gradient descent (SGD), as a special case.
Robustifying Binary Classification to Adversarial Perturbation
To this end, in this paper we consider the problem of binary classification with adversarial perturbations.
The Performance Analysis of Generalized Margin Maximizer (GMM) on Separable Data
We also provide a detailed study for three special cases: ($1$) $\ell_2$-GMM that is the max-margin classifier, ($2$) $\ell_1$-GMM which encourages sparsity, and ($3$) $\ell_{\infty}$-GMM which is often used when the parameter vector has binary entries.
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud.
The Impact of Regularization on High-dimensional Logistic Regression
In both cases, we obtain explicit expressions for various performance metrics and can find the values of the regularizer parameter that optimizes the desired performance.
Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
The problem of estimating an unknown signal, $\mathbf x_0\in \mathbb R^n$, from a vector $\mathbf y\in \mathbb R^m$ consisting of $m$ magnitude-only measurements of the form $y_i=|\mathbf a_i\mathbf x_0|$, where $\mathbf a_i$'s are the rows of a known measurement matrix $\mathbf A$ is a classical problem known as phase retrieval.
