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Volkan Cevher

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Publications associées (249)

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Volkan Cevher, Junhong Lin

We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...

2020

An adaptive primal-dual framework for nonsmooth convex minimization

Volkan Cevher, Quoc Tran Dinh, Ahmet Alacaoglu

We propose a new self-adaptive and double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov’s smoothing technique via general Bregman distance functions. It self-adaptiv ...

2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Kamalaruban Parameswaran, Yu-Ting Huang, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

34th Conference on Neural Information Processing Systems (NeurIPS 2020)2020

Scalable Learning-Based Sampling Optimization For Compressive Dynamic MRI

Ruud van Heeswijk, Armin Eftekhari, Volkan Cevher, Baran Gözcü, Thomas Sanchez

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 paper, we tackle the problem of designing a sampling mask for an arbitrary recon ...

IEEE2020

Convergence of the Exponentiated Gradient Method with Armijo Line Search

Volkan Cevher, Yen-Huan Li

Consider the problem of minimizing a convex differentiable function on the probability simplex, spectrahedron, or set of quantum density matrices. We prove that the expo-nentiated gradient method with Armijo line search always converges to the optimum, if ...

2019

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019

Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces

Volkan Cevher, Hatice Altug, Filiz Yesilköy, Andreas Tittl, Yasaman Jahani, Eduardo Romero Arvelo, Mingkai Liu

Metasurfaces based on resonant subwavelength photonic structures enable novel ways of wavefront control and light focusing, underpinning a new generation of flat-optics devices. Recently emerged all-dielectric asymmetric metasurfaces, composed of arrays of ...

Nature Publishing Group2019

A Learning-Based Framework for Quantized Compressed Sensing

Volkan Cevher, Rabeeh Karimi Mahabadi, Junhong Lin

Sparse recovery from undersampled random quan- tization measurements is a recent active research topic. Previous work asserts that stable recovery can be guaranteed via the basis pursuit dequantizer (BPDQ) if the measurements number is large enough, consid ...

IEEE Institute of Electrical and Electronics Engineers2019

An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints

Armin Eftekhari, Volkan Cevher, Ahmet Alacaoglu, Mehmet Fatih Sahin, Fabian Ricardo Latorre Gomez

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex problems with nonlinear constraints. We characterize the total computational complexity of our method subject to a verifiable geometric condition, which is closely related to t ...

2019

Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation

Volkan Cevher, Alp Yurtsever

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 collection. The technical contribution consists in a new algorithm for constructing ...

2019

On the convergence of stochastic primal-dual hybrid gradient

Volkan Cevher, Ahmet Alacaoglu

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 convergence of the iterates to a solution and linear convergence with standard ...

2019

A Conditional Gradient-Based Augmented Lagrangian Framework

Volkan Cevher, Alp Yurtsever

This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing conditional gradient methods either do not apply to our template or are too slow ...

2019

Chemical machine learning with kernels: The impact of loss functions

Volkan Cevher, Quoc Tran Dinh, Sandip De, Van Quang Nguyen, Junhong Lin

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations tha ...

2019

Interactive Teaching Algorithms for Inverse Reinforcement Learning

Volkan Cevher, Kamalaruban Parameswaran

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 algorithmic question: How could a teacher provide an informative sequence of demonst ...

2019

Efficient learning of smooth probability functions from Bernoulli tests with guarantees

Volkan Cevher, Paul Thierry Yves Rolland, Ali Kavis

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate ...

2019

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization

Volkan Cevher, Ali Kavis

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{simultaneously} achieves the optimal rates for smooth/non-smooth problems with eit ...

La Jolla2019

Interactive Teaching Algorithms for Inverse Reinforcement Learning

Volkan Cevher, Kamalaruban Parameswaran

2019

Stochastic Frank-Wolfe for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever

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 some affine constraints. The majority of classical SDP solvers are designed fo ...

2019

Iterative Classroom Teaching

Pierre Dillenbourg, Thibault Lucien Christian Asselborn, Volkan Cevher, Louis Pierre Faucon, Shuqing Teresa Yeo, Kamalaruban Parameswaran

We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rate ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019

Fast and Provable ADMM for Learning with Generative Priors

Armin Eftekhari, Volkan Cevher, Fabian Ricardo Latorre Gomez

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 the special case where such constraint arises from the specification that a ...

2019

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