Publications by Volkan Cevher | EPFL Graph Search

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Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regr ...

This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or ...

We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

We examine a flexible algorithmic framework for solving monotone variational inequalities in the presence of randomness and uncertainty. The proposed template encompasses a wide range of popular first-order methods, including dual averaging, dual extrapola ...

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 ...

We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that ar ...

We consider the problem of actively learning \textit{multi-index} functions of the form

f(x) = g(Ax)= \sum_{i=1}^k g_i(a_i^Tx)

from point evaluations of

f

. We assume that the function

f

is defined on an

\ell_2

-ball in

\Real^d

g

is twice contin ...

We study the generalization of iterative noisy gradient schemes on smooth non-convex losses. Formally, we establish time-independent information theoretic generalization bounds for Stochastic Gradient Langevin Dynamics (SGLD) that do not diverge as the ite ...

We propose a stochastic conditional gradient method (CGM) for minimizing convex finitesum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully inc ...

We consider the following expander-based compressive sensing (e-CS) problem: Given Φ ∈ ℝM×N (M

We introduce a randomly extrapolated primal-dual coordinate descent method that adapts to sparsity of the data matrix and the favorable structures of the objective function. Our method updates only a subset of primal and dual variables with sparse data, an ...

The self-concordant-like property of a smooth convex func- tion is a new analytical structure that generalizes the self-concordant notion. While a wide variety of important applications feature the self- concordant-like property, this concept has heretofor ...

We study the problem of actively learning a multi-index function of the form f (x) = g_0 (A_0 x) from its point evaluations, where A_0 ∈ R_{k×d} with k ≪ d. We build on the assumptions and techniques of an existing approach based on low-rank matrix recover ...

In this paper, we investigate the existence of online learning algorithms with bandit feedback that simultaneously guarantee O(1) regret compared to a given comparator strategy, and Õ(√ T) regret compared to any fixed strategy, where T is the number of rou ...

In this paper, we present and analyze a new set of low-rank recovery algorithms for linear inverse problems within the class of hard thresholding methods. We provide strategies on how to set up these algorithms via basic ingredients for different configura ...

We design a sublinear Fourier sampling algorithm for a case of sparse off-grid frequency recovery. These are signals with the form f(t)=∑kj=1ajeiωjt+ν^ , t∈Z ; i.e., exponential polynomials with a noise term. The frequencies {ω j } satisfy ω j ∈ [η,2π − η ...

In the multi-armed bandit literature, the multi-bandit best-arm identification problem consists of determining each best arm in a number of disjoint groups of arms, with as few total arm pulls as possible. In this paper, we introduce a variant of the multi ...

Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system’s area and power. In this paper, we ...

Volkan Cevher

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Truly No-Regret Learning in Constrained MDPs

DiGress: Discrete Denoising diffusion for graph generation

On the Double Descent of Random Features Models Trained with SGD

Estimation Error of the Constrained Lasso

Efficient Continual Finite-Sum Minimization

Sifting through the Noise: Universal First-Order Methods for Stochastic Variational Inequalities

Scalable Learning-Based Sampling Optimization For Compressive Dynamic MRI

Filtered Variation method for denoising and sparse signal processing

Active Learning of Multi-Index Function Models

Generalization of Noisy SGD in Unbounded Non-convex Settings

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

A Game Theoretic Approach to Expander-based Compressive Sensing

Random extrapolation for primal-dual coordinate descent

Composite convex minimization involving self-concordant-like cost functions

Active Learning of Self-concordant like Multi-index Functions

Best of Both Worlds: Regret Minimization versus Minimax Play

Matrix Recipes for Hard Thresholding Methods

What’s the Frequency, Kenneth?: Sublinear Fourier Sampling Off the Grid

Overlapping Multi-Bandit Best Arm Identification

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants