Prof. Volkan Cevher ‒ LIONS ‐ EPFL

Research Interests:

Machine Learning
Optimization
Signal Processing
Information Theory

Biography

Volkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara, Turkey, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta, GA in 2005. He was a Research Scientist with the University of Maryland, College Park, from 2006-2007 and also with Rice University in Houston, TX, from 2008-2009. He was also a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University from 2010-2020. Currently, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and an Amazon Scholar. His research interests include machine learning, optimization theory and methods, and automated control. Dr. Cevher is an IEEE Fellow (’24), an ELLIS fellow, and was the recipient of the ICML AdvML Best Paper Award in 2023, Google Faculty Research award in 2018, the IEEE Signal Processing Society Best Paper Award in 2016, a Best Paper Award at CAMSAP in 2015, a Best Paper Award at SPARS in 2009, and an ERC CG in 2016 as well as an ERC StG in 2011.

Publications (most recent)

Membership Inference Attacks against Large Vision-Language Models

Zhan Li; Y. Wu; Y. Chen; F. Tonin; E. Abad Rocamora et al.

2024. 38th Annual Conference on Neural Information Processing Systems, Vancouver Convention Center, 2024-12-10 – 2024-12-15.

Research Interests:

Biography

Publications (most recent)

Membership Inference Attacks against Large Vision-Language Models

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

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Truly No-Regret Learning in Constrained MDPs

Universal Gradient Methods for Stochastic Convex Optimization

On the Generalization of Stochastic Gradient Descent with Momentum

Revisiting Character-level Adversarial Attacks for Language Models

Improving SAM Requires Rethinking its Optimization Formulation

Robust NAS under adversarial training: benchmark, theory, and beyond

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Efficient local linearity regularization to overcome catastrophic overfitting

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Graph generative deep learning models with an application to circuit topologies

Efficient Continual Finite-Sum Minimization

Imitation Learning in Discounted Linear MDPs without exploration assumptions

Learning to Remove Cuts in Integer Linear Programming

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Maximum Independent Set: Self-Training through Dynamic Programming

Alternation makes the adversary weaker in two-player games

Federated Learning under Covariate Shifts with Generalization Guarantees

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