Publications de Volkan Cevher | EPFL Graph Search

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Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our ...

In this paper, we consider composite convex minimization problems. We advocate the merit of considering Generalized Proximal gradient Methods (GPM) where the norm employed is not Euclidean. To that end, we show the tractability of the general proximity ope ...

The measurement matrix employed in compressive sensing typically cannot be known precisely a priori, and must be estimated via calibration. One may take multiple compressive measurements, from which the measurement matrix and underlying signals may be esti ...

While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert’s behavior. It is well known that, in general, variou ...

We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadra ...

We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard converg ...

We propose a new randomized coordinate descent method for a convex optimization template with broad applications. Our analysis relies on a novel combination of four ideas applied to the primal-dual gap function: smoothing, acceleration, homotopy, and coord ...

The strong growth condition (SGC) is known to be a sufficient condition for linear convergence of the stochastic gradient method using a constant step-size γ (SGM-CS). In this paper, we provide a necessary condition, for the linear convergence of SGM-CS, t ...

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. A promise of these models is to lead to “interpretable” signals for which we identify i ...

We address the problem of microphone location cali- bration where the sensor positions have a sparse spatial approximation on a discretized grid. We characterize the microphone signals as a sparse vector represented over a codebook of multi-channel signals ...

We propose Matrix ALPS for recovering a sparse plus low-rank decomposition of a matrix given its corrupted and incomplete linear measurements. Our approach is a first-order projected gradient method over non-convex sets, and it exploits a well-known memory ...

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 ...

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not a ...

We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a novel algorithmic framework for GANs via an infinite-dimensional two-player ...

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case ...

We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We rst investigate regularized algorithms adapted to a projection operator on a closed subspace ...

We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct new primal-dual methods with optimal convergence rates on the objective residual and the primal fe ...

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 ...

Volkan Cevher

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Faster Coordinate Descent via Adaptive Importance Sampling

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements

Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning

A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions

Online Adaptive Methods, Universality and Acceleration

Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization

On the linear convergence of the stochastic gradient method with constant step-size

Tractability of interpretability via selection of group-sparse models

Structured Sparse Coding for Microphone Array Location Calibration

Matrix ALPS: Accelerated Low Rank and Sparse Matrix Reconstruction

On the convergence of stochastic primal-dual hybrid gradient

Proximal Point Imitation Learning

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Preconditioned Spectral Descent for Deep Learning

Finding Mixed Nash Equilibria of Generative Adversarial Networks

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Constrained convex minimization via model-based excessive gap

Interactive Teaching Algorithms for Inverse Reinforcement Learning