no code implementations • 25 May 2023 • Fabian Spaeh, Alina Ene, Huy L. Nguyen
Constrained $k$-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others.
no code implementations • 28 Feb 2023 • Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen, Alina Ene, Huy Lê Nguyen
Instead, we show high probability convergence with bounds depending on the initial distance to the optimal solution.
no code implementations • 3 Oct 2022 • Alina Ene, Huy L. Nguyen
In this work, we describe a generic approach to show convergence with high probability for stochastic convex optimization.
no code implementations • 29 Sep 2022 • Zijian Liu, Ta Duy Nguyen, Thien Hang Nguyen, Alina Ene, Huy L. Nguyen
There, STORM utilizes recursive momentum to achieve the VR effect and is then later made fully adaptive in STORM+ [Levy et al., '21], where full-adaptivity removes the requirement for obtaining certain problem-specific parameters such as the smoothness of the objective and bounds on the variance and norm of the stochastic gradients in order to set the step size.
no code implementations • 29 Sep 2022 • Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen
Finally, we give new accelerated adaptive algorithms and their convergence guarantee in the deterministic setting with explicit dependency on the problem parameters, improving upon the asymptotic rate shown in previous works.
1 code implementation • International Conference on Machine Learning 2022 • Alina Ene, Huy L. Nguyen
Maximizing a monotone k-submodular function subject to cardinality constraints is a general model for several applications ranging from influence maximization with multiple products to sensor placement with multiple sensor types and online ad allocation.
no code implementations • 28 Jan 2022 • Zijian Liu, Ta Duy Nguyen, Alina Ene, Huy L. Nguyen
To address this problem, we propose two novel adaptive VR algorithms: Adaptive Variance Reduced Accelerated Extra-Gradient (AdaVRAE) and Adaptive Variance Reduced Accelerated Gradient (AdaVRAG).
no code implementations • 22 Dec 2020 • Alina Ene, Huy L. Nguyen, Adrian Vladu
We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting.
no code implementations • 15 Oct 2020 • Alina Ene, Huy L. Nguyen
We show that our algorithms are universal and simultaneously achieve the optimal convergence rates in the non-smooth, smooth, and stochastic settings.
no code implementations • 17 Jul 2020 • Alina Ene, Huy L. Nguyen, Adrian Vladu
We provide new adaptive first-order methods for constrained convex optimization.
no code implementations • ICML 2020 • Alina Ene, Huy L. Nguyen
For any desired accuracy $\epsilon$, our algorithm achieves a $1/e - \epsilon$ approximation using $O(\log{n} \log(1/\epsilon) / \epsilon^3)$ parallel rounds of function evaluations.
no code implementations • 18 Feb 2019 • Alina Ene, Adrian Vladu
The iteratively reweighted least squares method (IRLS) is a popular technique used in practice for solving regression problems.
Data Structures and Algorithms
no code implementations • 4 Dec 2018 • Alina Ene, Huy L. Nguyen, Adrian Vladu
We study parallel algorithms for the problem of maximizing a non-negative submodular function.
no code implementations • NeurIPS 2017 • Alina Ene, Huy L. Nguyen, László A. Végh
This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization.
no code implementations • 27 Jun 2016 • Alina Ene, Huy L. Nguyen
A function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ is DR-submodular if it satisfies $f({\bf x} + \chi_i) -f ({\bf x}) \ge f({\bf y} + \chi_i) - f({\bf y})$ for all ${\bf x}\le {\bf y}, i\in E$.
no code implementations • 14 Jul 2015 • Rafael da Ponte Barbosa, Alina Ene, Huy L. Nguyen, Justin Ward
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems.
no code implementations • 9 Feb 2015 • Alina Ene, Huy L. Nguyen
Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision.
no code implementations • 9 Feb 2015 • Rafael da Ponte Barbosa, Alina Ene, Huy L. Nguyen, Justin Ward
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems.