# Volkan Cevher

**EPFL STI IEL LIONS **

ELE 233 (Bâtiment ELE)

Station 11

CH-1015 Lausanne

**EPFL STI SEL-GE **

ELE 233 (Bâtiment ELE)

Station 11

CH-1015 Lausanne

## Biography

Prof. Volkan Cevher received his BSc degree (valedictorian) in Electrical Engineering from Bilkent University in 1999, and his PhD degree in Electrical and Computer Engineering from Georgia Institute of Technology in 2005. He held Research Scientist positions at University of Maryland, College Park during 2006-2007 and at Rice University during 2008-2009. Currently, he is an Assistant Professor at Ecole Polytechnique Federale de Lausanne and a Faculty Fellow at Rice University. His research interests include signal processing theory, machine learning, graphical models, and information theory.

## Fields of expertise

Machine Learning

Optimization

Signal Processing

Information Theory

## Publications

#### Selected Publications

**Chemical machine learning with kernels: The impact of loss functions**;

*International Journal Of Quantum Chemistry*. 2019-05-05. DOI : 10.1002/qua.25872.

**Inertial Three-Operator Splitting Method and Applications**; SIAM Conference on Optimization - OP17, Vancouver, British Columbia, Canada, May 22-25, 2017.

**Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator**. 2019. 36th International Conference on Machine Learning (ICML 2019), Long Beach, USA, June 9-15, 2019.

**Efficient learning of smooth probability functions from Bernoulli tests with guarantees.**. 2019. 36th International Conference on Machine Learning (ICML 2019), Long Beach, USA, June 9-15, 2019.

**A Conditional Gradient-Based Augmented Lagrangian Framework**. 2019. 36th International Conference on Machine Learning (ICML 2019), Long Beach, USA, June 9-15, 2019.

**Almost surely constrained convex optimization**. 2019. 36th International Conference on Machine Learning (ICML 2019), Long Beach, USA, June 9-15, 2019.

**Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI**. 2019.

**Rethinking Sampling in Parallel MRI: A Data-Driven Approach**. 2019.

**Overlapping Multi-Bandit Best Arm Identification**. 2019. The 2019 IEEE International Symposium on Information Theory (ISIT), Paris, France, July 7-12, 2019.

**Iterative Classroom Teaching**. 2019. 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, January 27 – February 1, 2019.

**A Learning-Based Framework for Quantized Compressed Sensing**. 2019. A Learning-Based Framework for Quantized Compressed Sensing.

**A Learning-Based Framework for Quantized Compressed Sensing**;

*IEEE Signal Processing Letters*. 2019.

**Robust Adaptive Decision Making: Bayesian Optimization and Beyond**. Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9147.

**Convergence of the Exponentiated Gradient Method with Armijo Line Search**;

*Journal of Optimization Theory and Applications*. 2018-12-03. DOI : 10.1007/s10957-018-1428-9.

**A Single-Phase, Proximal Path-Following Framework**;

*Mathematics Of Operations Research*. 2018-11-01. DOI : 10.1287/moor.2017.0907.

**Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces**;

*Applied and Computational Harmonic Analysis*. 2018-10-04. DOI : 10.1016/j.acha.2018.09.009.

**Near-Optimal Noisy Group Testing via Separate Decoding of Items**;

*IEEE Journal of Selected Topics In Signal Processing*. 2018-10-01. DOI : 10.1109/JSTSP.2018.2844818.

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

*Optimization Letters*. 2018-09-25. DOI : 10.1007/s11590-018-1331-1.

**Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms**. 2018-09-03.

**A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming**. 2018-07-11. the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, July 10-15, 2018.

**Online Adaptive Methods, Universality and Acceleration**. 2018-07-04. 32nd Conference on Neural Information Processing Systems conference (NIPS 2018), Montreal, Canada, December 3-8, 2018.

**Near-Optimal Noisy Group Testing via Separate Decoding of Items**. 2018-06-17. IEEE International Symposium on Information Theory, Colorado, USA., June 17-22. 2018. p. 2311-2315.

**Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods**. 2018-06-08. 35th International Conference on Machine Learning, Stockholm, Sweden, July 10 -15, 2018.

**Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces**. 2018-03-11. 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, July 10-15, 2018.

**A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization**;

*SIAM Journal on Optimization*. 2018-01-11. DOI : 10.1137/16M1093094.

**Real-time DCT Learning-based Reconstruction of Neural Signals**. 2018-01-01. European Signal Processing Conference (EUSIPCO), Rome, ITALY, Aug 03-07, 2018. p. 1925-1929.

**An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems**. 2018-01-01. 15th ACM International Conference on Computing Frontiers, Ischia, ITALY, May 08-10, 2018. p. 228-231. DOI : 10.1145/3203217.3203260.

**Phonetic Subspace Features for Improved Query by Example Spoken Term Detection**;

*Speech Communication*. 2018. DOI : 10.1016/j.specom.2018.07.001.

**Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization**;

*Large-Scale and Distributed Optimization*; Springer, 2018.

**Sparse Linear Inversion for Strong Gravitational Lenses Reconstruction, Component Separation from Morphological Component Analysis with Multiple Discrimination Criteria**. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8965.

**Adversarially Robust Optimization with Gaussian Processes**. 2018. Conference on Neural Information Processing Systems (NIPS), Montreal, 2018.

**Finding Mixed Nash Equilibria of Generative Adversarial Networks**. 2018.

**Real-time DCT Learning-based Reconstruction of Neural Signals**. 2018. 26th European Signal Processing Conference (EUSIPCO 2018), Rome, Italy , September 3-7. 2018.

**Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants**. 2018. 1st International Symposium on Integrated Circuits and Systems (ISICAS), Taormina, ITALY, Sep 02-03, 2018. p. 3929-3941. DOI : 10.1109/TCSI.2018.2853983.

**An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems**. 2018. ACM International Conference on Computing Frontiers 2018, Ischia, Italy, May 8-10, 2018.

**Learning-Based Hardware Design for Data Acquisition Systems**. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8693.

**Learning without Smoothness and Strong Convexity**. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8765.

**Learning with Structured Sparsity: From Discrete to Convex and Back.**. EPFL, 2018. DOI : 10.5075/epfl-thesis-8516.

**Learning-Based Compressive MRI**;

*IEEE Transactions On Medical Imaging*. 2018. DOI : 10.1109/TMI.2018.2832540.

**Mirrored Langevin Dynamics**. 2018. Thirty-second Conference on Neural Information Processing Systems (NIPS), Montréal.

**Dimension-free Information Concentration via Exp-Concavity**. 2018. Algorithmic Learning Theory (ALT) 2018, Lanzarote, Spain, April 7-9, 2018.

**Robust Maximization of Non-Submodular Objectives**. 2018. International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Canary Islands, April, 9-11, 2018.

**High Dimensional Bayesian Optimization via Additive Models with Overlapping Groups**. 2018. AISTATS, Lanzarote, Spain, April, 9-11, 2018.

**Stochastic Three-Composite Convex Minimization with a Linear Operator**. 2018. 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018,, Lanzarotte, Spain, April 9-11, 2018.

**Let’s be honest: An optimal no-regret framework for zero-sum games**. 2018. 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, July 10-15, 2018.

**Chemical machine learning with kernels: The key impact of loss functions**. 2018.

**An Eight lanes 7Gb/s/pin Source Synchronous Single-Ended RX with Equalization and Far-End Crosstalk Cancellation for Backplane Channels**;

*IEEE Journal of Solid State Circuits*. 2018. DOI : 10.1109/JSSC.2017.2783679.

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

*IEEE Transactions on Signal Processing*. 2018. DOI : 10.1109/TSP.2018.2870353.

**Stochastic Forward-Douglas-Rachford Splitting for Monotone Inclusions**;

*Stochastic Forward Douglas-Rachford Splitting Method for Monotone Inclusions*; Springer International Publishing, 2018.

**Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data**. 2017-12-04. 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, California, USA, December 4-9, 2017.

**Practical Sketching Algorithms For Low-Rank Matrix Approximation**;

*Siam Journal On Matrix Analysis And Applications*. 2017. DOI : 10.1137/17M1111590.

**Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach**. 2017. Conference on Neural Information Processing Systems (NIPS), Long Beach.

**Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization**. 2017. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, December 4-9, 2017.

**A Distributed Algorithm for Partitioned Robust Submodular Maximization**. 2017. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

**Phase Transitions in the Pooled Data Problem**. 2017. Conference on Neural Information Processing Systems (NIPS), Long Beach, California, December 2017.

**Efficient and Near-Optimal Noisy Group Testing: An Information-Theoretic Framework**;

*IEEE Journal of Selected Topics in Signal Processing*. 2017.

**Combinatorial Penalties: Which structures are preserved by convex relaxations?**. 2017. 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarotte, Spain , April 9-11, 2017.

**General Proximal Gradient Method: A Case for Non-Euclidean Norms**. 2017.

**Smoothing technique for nonsmooth composite minimization with linear operator**;

*Preprint*. 2017.

**Robust Submodular Maximization: A Non-Uniform Partitioning Approach**. 2017. The 34th International Conference on Machine Learning (ICML), Sydney, 2017.

**Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization**. 2017. Conference on Learning Theory (COLT), Amsterdam, July 2017.

**DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems**. 2017. Computing Frontiers Conference 2017, Siena, Italy, May 15-17, 2017. p. 391-394. DOI : 10.1145/3075564.3078890.

**Learning-based subsampling**. US2017109650 . 2017.

**An Adaptive Sublinear-Time Block Sparse Fourier Transform**. 2017. ACM Symposium on Theory of Computing (STOC), Montreal, June 19-23, 2017.

**Faster Coordinate Descent via Adaptive Importance Sampling**. 2017. 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA, April 20-22, 2017.

**Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage**. 2017. 20th International Conference on Artificial Intelligence and Statistics (AISTATS2017), Fort Lauderdale, Florida, USA, April 20-22, 2017.

**How little does non-exact recovery help in group testing?**. 2017. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, March 2017. p. 6090-6094.

**A single-phase, proximal path-following framework**;

*Mathematics of Operations Research*. 2017.

**Lower Bounds on Active Learning for Graphical Model Selection**. 2017. The 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), Fort Lauderdale, Florida, USA, April 20-22, 2017.

**Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework**;

*IEEE Transactions on Information Theory*. 2017. DOI : 10.1109/TIT.2016.2606605.

**An Efficient Streaming Algorithm for the Submodular Cover Problem**. 2016. The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS).

**Stochastic Three-Composite Convex Minimization**. 2016. 30th Conference on Neural Information Processing Systems (NIPS2016), Barcelona, Spain, December 5-10, 2016.

**Randomized Single-View Algorithms for Low-Rank Matrix Approximation**. 2016.

**Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation**. 2016. Conference on Neural Information Processing Systems (NIPS), Barcelona, December 5-10, 2016.

**Efficient Incremental Data Analysis**. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7183.

**Convex block-sparse linear regression with expanders - provably**. 2016. The 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), Cadiz, Spain, May 7-11, 2016.

**From Massive Parallelization to Quantum Computing: Seven Novel Approaches to Query Optimization**. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6995.

**Estimation Error of the Constrained Lasso**. 2016. 54th Annu. Allerton Conf. Communication, Control, and Computing, Monticello, IL, September 27-30, 2016.

**Learning Data Triage: Linear Decoding Works for Compressive MRI**. 2016. 41st IEEE International Conference on Acoustics, Speech and Signal Processing. p. 4034-4038.

**Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval**. 2016. 41st IEEE International Conference on Acoustics, Speech and Signal Processing. p. 6230-6234.

**Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition**. 2016. 26th edition of GLSVLSI, Boston, USA, May 18-20, 2016. p. 433-438. DOI : 10.1145/2902961.2903028.

**Time-Varying Gaussian Process Bandit Optimization**. 2016. International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May 9 - 11, 2016.

**Partial Recovery Bounds for the Sparse Stochastic Block Model**. 2016. International Symposium on Information Theory (ISIT), Barcelona, July 10-15, 2016. p. 1904-1908.

**Converse Bounds for Noisy Group Testing with Arbitrary Measurement Matrices**. 2016. International Symposium on Information Theory (ISIT), Barcelona, July 10-15, 2016. p. 2868-2872.

**On the Difficulty of Selecting Ising Models with Approximate Recovery**;

*IEEE Transactions on Signal and Information Processing over Networks*. 2016. DOI : 10.1109/Tsipn.2016.2596439.

**Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices**. 2016. International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May 9-11, 2016.

**Adaptive-Rate Reconstruction of Time-Varying Signals with Application in Compressive Foreground Extraction**;

*IEEE Transactions on Signal Processing*. 2016. DOI : 10.1109/TSP.2016.2544744.

**Stochastic Spectral Descent for Discrete Graphical Models**;

*IEEE Journal of Selected Topics in Signal Processing*. 2016. DOI : 10.1109/Jstsp.2015.2505684.

**Learning-Based Compressive Subsampling**;

*IEEE Journal on Selected Topics in Signal Processing*. 2016. DOI : 10.1109/Jstsp.2016.2548442.

**Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization**;

*Ieee Transactions On Signal Processing*. 2016. DOI : 10.1109/Tsp.2015.2488598.

**Computational Methods for Underdetermined Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis**;

*Speech Communication*. 2016. DOI : 10.1016/j.specom.2015.07.002.

**Phase Transitions in Group Testing**. 2016. ACM-SIAM Symposium on Discrete Algorithms (SODA), Arlington, Virginia, USA, January 10-12, 2016.

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

*IEEE Transactions on Signal Processing*. 2016. DOI : 10.1109/TSP.2016.2544744.

**Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices**. 2016. IEEE International Symposium on Information Theory, (ISIT), Istanbul, Turkey, July 7-12 2013. p. 7464-7474. DOI : 10.1109/ISIT.2013.6620309.

**Group-Sparse Model Selection: Hardness and Relaxations**;

*IEEE Transactions on Information Theory*. 2016. DOI : 10.1109/TIT.2016.2602222.

**Composite convex minimization involving self-concordant-like cost functions**. 2015. Modelling, Computation and Optimization in Information Systems and Management Sciences (MCO 2015), Metz, France, May 11-13, 2015.

**Structured Sampling and Recovery of iEEG Signals**. 2015. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Cancun, Mexico, December 13-16, 2015.

**Scalable Convex Methods for Phase Retrieval**. 2015. 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Cancun, Mexico, December 13-16, 2015.

**Introduction to the Issue on Signal Processing for Big Data**;

*Ieee Journal Of Selected Topics In Signal Processing*. 2015. DOI : 10.1109/Jstsp.2015.2418393.

**Preconditioned Spectral Descent for Deep Learning**. 2015. 29-th Neural Information Processing Systems (NIPS), 2015.

## Teaching & PhD

#### Teaching

- Electrical and Electronics Engineering,

#### PhD Programs

- Doctoral Program in Electrical Engineering
- Doctoral program in computer and communication sciences
- Doctoral Program in Microsystems and Microelectronics
- Doctoral Program Digital Humanities

#### PhD Students

Gözcü Baran

Hsieh Ya-Ping

Kavis Ali

Latorre Gomez Fabian Ricardo

Rolland Paul Thierry Yves

Sahin Mehmet Fatih

Sanchez Thomas

Uran Arda

Yurtsever Alp

#### Past PhD Students

Aprile Cosimo ...Bogunovic Ilija ...

El Halabi Marwa ...

Kyrillidis Anastasios ...

Li Yen-Huan ...

## Courses

#### Mathematics of data: from theory to computation

This course reviews recent advances in convex optimization and statistical analysis in the wake of Big Data. We provide an overview of the emerging convex formulations and their guarantees, describe scalable solution techniques, and illustrate the role of...

#### Theory and Methods for Reinforcement Learning

This course describes theory and methods for decision making under uncertainty under partial feedback.

#### EECS Seminar: Advanced Topics in Machine Learning

Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theore...