Publications de Volkan Cevher | EPFL Graph Search

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We consider monotone variational inequality (VI) problems in multi-GPU settings where multiple processors/workers/clients have access to local stochastic dual vectors. This setting includes a broad range of important problems from distributed convex minimi ...

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

In this paper, we focus on a theory-practice gap for Adam and its variants (AMSgrad, AdamNC, etc.). In practice, these algorithms are used with a constant first-order moment parameter 1 (typically between 0:9 and 0:99). In theory, regret guarantees for onl ...

We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environment. In particular, we re- move exploration assu ...

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. O ...

We study the problem of maximizing a monotone submodular function subject to a cardinality constraint

k

, with the added twist that a number of items

\tau

from the returned set may be removed. We focus on the worst-case setting considered in Orlin et al ...

Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic ...

Despite progress in adversarial training (AT), there is a substantial gap between the topperforming and worst-performing classes in many datasets. For example, on CIFAR10, the accuracies for the best and worst classes are 74% and 23%, respectively. We argu ...

We study robust federated learning (FL) within a game theoretic framework to alleviate the server vulnerabilities to even an informed adversary who can tailor training-time attacks (Fang et al., 2020; Xie et al., 2020a; Ozfatura et al., 2022; Rodríguez-Bar ...

We study convex optimization problems that feature low-rank matrix solutions. In such scenarios, non-convex methods offer significant advantages over convex methods due to their lower space complexity as well as faster convergence speed. Moreover, many of ...

EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introduces a novel graph-based residual state update mechanism (REST) for real-tim ...

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 ...

A Successive Approximation Register Analog-to-Digital Converter (SAR ADC) comprising a Track-and-Hold (T/H) switch configured for a sampling of an input voltage in the form of charge onto a capacitor; a Capacitive Digital-to- Analog Converter (CD AC) opera ...

We develop a primal-dual convex minimization framework to solve a class of stochastic convex three-composite problem with a linear operator. We consider the cases where the problem is both convex and strongly convex and analyze the convergence of the propo ...

This paper provides statistical sample complexity bounds for score-matching and its applications in causal discovery. We demonstrate that accurate estimation of the score function is achievable by training a standard deep ReLU neural network using stochast ...

We introduce a new sparse recovery paradigm, called Normed Pursuits, where efficient algorithms from combinatorial and convex optimization interface for interpretable and model-based solutions. Synthetic and real data experiments illustrate that Normed Pur ...

Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a p ...

Volkan Cevher

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Distributed Extra-Gradient With Optimal Complexity And Communication Guarantees

Manifold Sparse Beamforming

A new regret analysis for Adam-type algorithms

Imitation Learning in Discounted Linear MDPs without exploration assumptions

Convergence of adaptive algorithms for constrained weakly convex optimization

Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Foreground Subtraction

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation

Revisiting adversarial training for the worst-performing class

Mixed Nash for Robust Federated Learning

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates

Fast and Provable ADMM for Learning with Generative Priors

Asynchronous sar adc with unit length capacitors and constant common mode monotonic switching

Stochastic Three-Composite Convex Minimization with a Linear Operator

Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling

Hard Thresholding with Norm Constraints

The spectral bias of polynomial neural networks

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Structured Sampling and Recovery of iEEG Signals