16 citations · 77 across the 28 of their papers we have counts for
34 papers
Gap-free Differentially Private PCA for Gaussian Data
Alina Ene, Huy L. Nguyen
We give a gap-free -differentially private algorithm for the principal component analysis (PCA) problem with Gaussian data. The algorithm is based on a private variant of th…
Efficient Algorithms for Influence Maximization in General Models and Observed Cascades
Fabian Spaeh, Themistoklis Haris, Alina Ene +1
We study influence maximization in general stochastic models, the observed cascades model, and the independent cascade (IC) model. For general stochastic models with only black-box…
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…
Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures
Alina Ene, Alessandro Epasto, Vahab Mirrokni +4
In the maximum coverage problem we are given subsets from a universe , and the goal is to output subsets such that their union covers the largest possible number of di…
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…
Online and Streaming Algorithms for Constrained -Submodular Maximization
Fabian Spaeh, Alina Ene, Huy L. Nguyen
Constrained -submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recomme…
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…
High Probability Convergence for Accelerated Stochastic Mirror Descent
Alina Ene, Huy L. Nguyen
In this work, we describe a generic approach to show convergence with high probability for stochastic convex optimization. In previous works, either the convergence is only in expe…
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…
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…
Optimal Streaming Algorithms for Submodular Maximization with Cardinality Constraints
Naor Alaluf, Alina Ene, Moran Feldman +2
We study the problem of maximizing a non-monotone submodular function subject to a cardinality constraint in the streaming model. Our main contribution is a single-pass (semi-)stre…
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
Alina Ene, Huy L. Nguyen
In this work, we give a new parallel algorithm for the problem of maximizing a non-monotone diminishing returns submodular function subject to a cardinality constraint. For any des…
Improved Convergence for and Regression via Iteratively Reweighted Least Squares
Alina Ene, Adrian Vladu
The iteratively reweighted least squares method (IRLS) is a popular technique used in practice for solving regression problems. Various versions of this method have been proposed,…
A Parallel Double Greedy Algorithm for Submodular Maximization
Alina Ene, Huy L. Nguyen, Adrian Vladu
We study parallel algorithms for the problem of maximizing a non-negative submodular function. Our main result is an algorithm that achieves a nearly-optimal approximation…