14 citations · 41 across the 14 of their papers we have counts for
5 papers · 1 filter
Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds
Jonathan Lacotte, Mert Pilanci
We propose novel randomized optimization methods for high-dimensional convex problems based on restrictions of variables to random subspaces. We consider oblivious and data-adaptiv…
The Hidden Convex Optimization Landscape of Two-Layer ReLU Neural Networks: an Exact Characterization of the Optimal Solutions
Yifei Wang, Jonathan Lacotte, Mert Pilanci
We prove that finding all globally optimal two-layer ReLU neural networks can be performed by solving a convex optimization program with cone constraints. Our analysis is novel, ch…
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
Jonathan Lacotte, Mert Pilanci
We propose a new randomized algorithm for solving L2-regularized least-squares problems based on sketching. We consider two of the most popular random embeddings, namely, Gaussian…
Optimal Randomized First-Order Methods for Least-Squares Problems
Jonathan Lacotte, Mert Pilanci
We provide an exact analysis of a class of randomized algorithms for solving overdetermined least-squares problems. We consider first-order methods, where the gradients are pre-con…
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform
Jonathan Lacotte, Sifan Liu, Edgar Dobriban +1
Random projections or sketching are widely used in many algorithmic and learning contexts. Here we study the performance of iterative Hessian sketch for least-squares problems. By…