1.3k citations · 3.8k across the 222 of their papers we have counts for
18 papers · 1 filter
Scalable Semidefinite Programming
Alp Yurtsever, Joel A. Tropp, Olivier Fercoq +2
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct ra…
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
Donghwan Lee, Niao He, Parameswaran Kamalaruban +1
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and coo…
On the convergence of stochastic primal-dual hybrid gradient
Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher
In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure co…
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
Ali Kavis, Kfir Y. Levy, Francis Bach +1
We propose a novel adaptive, accelerated algorithm for the stochastic constrained convex optimization setting. Our method, which is inspired by the Mirror-Prox method, \emph{simult…
Nearly Minimal Over-Parametrization of Shallow Neural Networks
Armin Eftekhari, ChaeHwan Song, Volkan Cevher
A recent line of work has shown that an overparametrized neural network can perfectly fit the training data, an otherwise often intractable nonconvex optimization problem. For (ful…
A reflected forward-backward splitting method for monotone inclusions involving Lipschitzian operators
Volkan Cevher, Bang Cong Vu
The proximal extrapolated gradient method \cite{Malitsky18a} is an extension of the projected reflected gradient method \cite{Malitsky15}. Both methods were proposed for solving th…
Fast and Provable ADMM for Learning with Generative Priors
Fabian Latorre Gómez, Armin Eftekhari, Volkan Cevher
In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on…
Interactive Teaching Algorithms for Inverse Reinforcement Learning
Parameswaran Kamalaruban, Rati Devidze, Volkan Cevher +1
We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorith…
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
Mehmet Fatih Sahin, Armin Eftekhari, Ahmet Alacaoglu +2
We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex problems with nonlinear constraints. We characterize the total computational complexity of our metho…
Inertial Three-Operator Splitting Method and Applications
Volkan Cevher, Bang Cong Vu, Alp Yurtsever
We introduce an inertial variant of the forward-Douglas-Rachford splitting and analyze its convergence. We specify an instance of the proposed method to the three-composite convex…
On Certifying Non-uniform Bound against Adversarial Attacks
Chen Liu, Ryota Tomioka, Volkan Cevher
This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In con…
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collect…
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
Lijun Ding, Alp Yurtsever, Volkan Cevher +2
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form. This method is particularly effective for weakly con…
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
Thomas Sanchez, Baran Gözcü, Ruud B. van Heeswijk +4
Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data. In this pape…
Almost surely constrained convex optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara +1
We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need t…
A Conditional Gradient-Based Augmented Lagrangian Framework
Alp Yurtsever, Olivier Fercoq, Volkan Cevher
This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing cond…
Stochastic Frank-Wolfe for Composite Convex Minimization
Francesco Locatello, Alp Yurtsever, Olivier Fercoq +1
A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to so…
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation
Jonathan Scarlett, Volkan Cevher
Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas s…