16 citations · 77 across the 28 of their papers we have counts for
8 papers · 1 filter
META-STORM: Generalized Fully-Adaptive Variance Reduced SGD for Unbounded Functions
Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen +2
We study the application of variance reduction (VR) techniques to general non-convex stochastic optimization problems. In this setting, the recent work STORM [Cutkosky-Orabona '19]…
On the Convergence of AdaGrad(Norm) on : Beyond Convexity, Non-Asymptotic Rate and Acceleration
Zijian Liu, Ta Duy Nguyen, Alina Ene +1
Existing analysis of AdaGrad and other adaptive methods for smooth convex optimization is typically for functions with bounded domain diameter. In unconstrained problems, previous…
Projection-Free Bandit Optimization with Privacy Guarantees
Alina Ene, Huy L. Nguyen, Adrian Vladu
We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting. This setting is important whenever the decision set has a com…
Adaptive and Universal Algorithms for Variational Inequalities with Optimal Convergence
Alina Ene, Huy L. Nguyen
We develop new adaptive algorithms for variational inequalities with monotone operators, which capture many problems of interest, notably convex optimization and convex-concave sad…
Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities
Alina Ene, Huy L. Nguyen, Adrian Vladu
We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms AdaACSA and AdaAGD+ are accelerated methods, which are universal in the sense t…
Decomposable Submodular Function Minimization: Discrete and Continuous
Alina Ene, Huy L. Nguyen, László A. Végh
This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization. We provide improved running time estimates for the…
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
Alina Ene, Huy L. Nguyen
Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solva…
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
Rafael da Ponte Barbosa, Alina Ene, Huy L. Nguyen +1
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization prob…