147 citations · 445 across the 31 of their papers we have counts for
33 papers
Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting
Karthik Prakhya, Alp Yurtsever
We show that the training problem of a deep linear neural network under the squared loss admits an exact convex reformulation in a lifted space over a generalized completely positi…
Universal Adaptive Proximal Gradient Methods via Gradient Mapping Accumulation
Zimeng Wang, Alp Yurtsever
We propose an adaptive proximal gradient method for minimizing the sum of two functions, where one is a simple convex function, and the other belongs to one of the three classes: n…
The Multi-Block DC Function Class: Theory, Algorithms, and Applications
Pouria Fatemi, Hoomaan Maskan, Alp Yurtsever +1
We present the Multi-Block DC (BDC) class, a rich class of structured nonconvex functions that admit a DC ("difference-of-convex") decomposition across parameter blocks. This multi…
Generalized Stochastic Gradient Descent with Momentum Methods for Smooth Optimization
Zimeng Wang, Alp Yurtsever
Stochastic gradient descent with momentum (SGDM) methods have become fundamental optimization tools in machine learning, combining the computational efficiency of stochastic gradie…
Improved Rates for Stochastic Variance-Reduced Difference-of-Convex Algorithms
Anh Duc Nguyen, Alp Yurtsever, Suvrit Sra +1
In this work, we propose and analyze DCA-PAGE, a novel algorithm that integrates the difference-of-convex algorithm (DCA) with the ProbAbilistic Gradient Estimator (PAGE) to solve…
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning
Karlo Palenzuela, Ali Dadras, Alp Yurtsever +1
Multiple local steps are key to communication-efficient federated learning. However, theoretical guarantees for such algorithms, without data heterogeneity-bounding assumptions, ha…