37 citations · 56 across the 14 of their papers we have counts for
14 papers
Solving Positive Linear Programs with Differential Privacy
Alina Ene, Huy Le Nguyen, Ta Duy Nguyen +1
We study differentially private approximation algorithms for positive linear programs (LPs with nonnegative coefficients and variables), focusing on the fundamental families of pac…
Adaptive Power Iteration Method for Differentially Private PCA
Ta Duy Nguyen, Alina Ene, Huy Le Nguyen
We study -differentially private algorithms for the problem of approximately computing the top singular vector of a matrix where each…
Quasi-Self-Concordant Optimization with Lewis Weights
Alina Ene, Ta Duy Nguyen, Adrian Vladu
In this paper, we study the problem for a quasi-self-concordant function , where are $…
Improved Regression via Iteratively Reweighted Least Squares
Alina Ene, Ta Duy Nguyen, Adrian Vladu
We introduce fast algorithms for solving regression problems using the iteratively reweighted least squares (IRLS) method. Our approach achieves state-of-the-art iterati…
Solving Linear Programs with Differential Privacy
Alina Ene, Huy Le Nguyen, Ta Duy Nguyen +1
We study the problem of solving linear programs of the form , with differential privacy. For homogeneous LPs , we give an efficient -differentially…
Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems
Ta Duy Nguyen, Alina Ene
We study the densest subgraph problem and give algorithms via multiplicative weights update and area convexity that converge in and $O\left(\fr…
Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tails
Ta Duy Nguyen, Alina Ene, Huy L. Nguyen
In this work, we study the convergence \emph{in high probability} of clipped gradient methods when the noise distribution has heavy tails, ie., with bounded th moments, for some…
High Probability Convergence of Clipped-SGD Under Heavy-tailed Noise
Ta Duy Nguyen, Thien Hang Nguyen, Alina Ene +1
While the convergence behaviors of stochastic gradient methods are well understood \emph{in expectation}, there still exist many gaps in the understanding of their convergence with…
High Probability Convergence of Stochastic Gradient Methods
Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen +2
In this work, we describe a generic approach to show convergence with high probability for both stochastic convex and non-convex optimization with sub-Gaussian noise. In previous w…
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…
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Zijian Liu, Ta Duy Nguyen, Alina Ene +1
In this paper, we study the finite-sum convex optimization problem focusing on the general convex case. Recently, the study of variance reduced (VR) methods and their accelerated v…
On Adversarial Bias and the Robustness of Fair Machine Learning
Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda +2
Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing discrimination against a group, fair machine learning algorithms strive to equalize the behav…
Fast Genetic Algorithms
Benjamin Doerr, Huu Phuoc Le, Régis Makhmara +1
For genetic algorithms using a bit-string representation of length~, the general recommendation is to take as mutation rate. In this work, we discuss whether this is reall…