Search Results for author:
Found 6 papers, 2 papers with code
Hierarchical Verification for Adversarial Robustness
We show that, under certain conditions on the algorithm parameters, LayerCert provably reduces the number and size of the convex programs that one needs to solve compared to GeoCert.
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization
When applied to the distributed dual ERM problem, unlike state of the art that takes only the block-diagonal part of the Hessian, our approach is able to utilize global curvature information and is thus magnitudes faster.
An Efficient Pruning Algorithm for Robust Isotonic Regression
We study a generalization of the classic isotonic regression problem where we allow separable nonconvex objective functions, focusing on the case of estimators used in robust regression.
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
Initial computational results on convex problems demonstrate that our method significantly improves on communication cost and running time over the current state-of-the-art methods.
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms
We study the norms obtained from extending the k-support norm and OWL norms to the setting in which there are overlapping groups.
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems
Using a recent construction of Goemans (2010), we show that when optimizing over the convex hull of the permutation vectors (the permutahedron), we can reduce the number of variables and constraints to $\Theta(n \log n)$ in theory and $\Theta(n \log^2 n)$ in practice.
