Volkan Cevher

Q: What Schools Are Affiliated With Volkan Cevher

Volkan Cevher is affiliated with the following schools: Rice University, École Polytechnique Fédérale de Lausanne, Georgia Tech

Volkan Cevher's AcademicInfluence.com Rankings

Volkan Cevher

Engineering

#5642

World Rank

#6905

Historical Rank

Electrical Engineering

#1591

World Rank

#1686

Historical Rank

engineering Degrees

Volkan Cevher

Philosophy

#7793

World Rank

#11050

Historical Rank

Logic

#4830

World Rank

#6159

Historical Rank

philosophy Degrees

Download Badge

Engineering
Philosophy

Why Is Volkan Cevher Influential?

(Suggest an Edit or Addition)

(See a Problem?)

Volkan Cevher's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Model-Based Compressive Sensing (2008) (1668)
Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces (2019) (388)
Compressive Sensing for Background Subtraction (2008) (337)
Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics (2014) (304)
Sparse Signal Recovery Using Markov Random Fields (2008) (195)
Bilinear Generalized Approximate Message Passing—Part I: Derivation (2013) (184)
Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective (2010) (178)
Distributed target localization via spatial sparsity (2008) (163)
High-Dimensional Gaussian Process Bandits (2013) (159)
A compressive beamforming method (2008) (153)
Practical Sketching Algorithms for Low-Rank Matrix Approximation (2016) (146)
WASP: Scalable Bayes via barycenters of subset posteriors (2015) (143)
Submodular Dictionary Selection for Sparse Representation (2010) (130)
Learning with Compressible Priors (2009) (125)
Vehicle Speed Estimation Using Acoustic Wave Patterns (2009) (117)
Recovery of Clustered Sparse Signals from Compressive Measurements (2009) (105)
Learning-Based Compressive MRI (2018) (97)
Composite self-concordant minimization (2013) (94)
Compressible Distributions for High-Dimensional Statistics (2011) (91)
Sparse projections onto the simplex (2012) (91)
Adversarially Robust Optimization with Gaussian Processes (2018) (89)
Limits on Support Recovery With Probabilistic Models: An Information-Theoretic Framework (2015) (87)
Matrix Recipes for Hard Thresholding Methods (2012) (87)
Phase Transitions in Group Testing (2016) (84)
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization (2020) (83)
Fixed Points of Generalized Approximate Message Passing With Arbitrary Matrices (2013) (81)
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage (2017) (80)
Compressive wireless arrays for bearing estimation (2008) (79)
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups (2018) (78)
Bearing Estimation via Spatial Sparsity using Compressive Sensing (2012) (75)
Scalable Semidefinite Programming (2019) (74)
Learning-Based Compressive Subsampling (2015) (73)
A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization (2015) (72)
Sparse Signal Recovery and Acquisition with Graphical Models (2010) (69)
A variational approach to stable principal component pursuit (2014) (69)
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization (2017) (68)
Compressive sensing recovery of spike trains using a structured sparsity model (2009) (65)
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation (2016) (64)
Greedy Dictionary Selection for Sparse Representation (2011) (64)
Time-Varying Gaussian Process Bandit Optimization (2016) (63)
Online Adaptive Methods, Universality and Acceleration (2018) (62)
Sensor array calibration via tracking with the extended Kalman filter (2001) (62)
Target Tracking Using a Joint Acoustic Video System (2007) (61)
Bilinear Generalized Approximate Message Passing—Part II: Applications (2014) (60)
Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces (2018) (59)
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation (2019) (59)
Finding Mixed Nash Equilibria of Generative Adversarial Networks (2018) (58)
A Universal Primal-Dual Convex Optimization Framework (2015) (57)
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data (2017) (55)
Model-based compressive sensing for signal ensembles (2009) (55)
Recipes on hard thresholding methods (2011) (54)
Optimization for Reinforcement Learning: From a single agent to cooperative agents (2019) (53)
Convexity in Source Separation : Models, geometry, and algorithms (2013) (52)
A Primal-Dual Algorithmic Framework for Constrained Convex Minimization (2014) (52)
Mirrored Langevin Dynamics (2018) (52)
Near-optimal Bayesian localization via incoherence and sparsity (2009) (52)
Structured Sparsity Models for Reverberant Speech Separation (2014) (49)
Interactive Teaching Algorithms for Inverse Reinforcement Learning (2019) (49)
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems (2020) (49)
Robust Submodular Maximization: A Non-Uniform Partitioning Approach (2017) (49)
Compressive sensing under matrix uncertainties: An Approximate Message Passing approach (2011) (49)
The limits of min-max optimization algorithms: convergence to spurious non-critical sets (2020) (46)
Compressed sensing for multi-view tracking and 3-D voxel reconstruction (2008) (43)
Combinatorial selection and least absolute shrinkage via the Clash algorithm (2012) (43)
Learning Non-Parametric Basis Independent Models from Point Queries via Low-Rank Methods (2013) (42)
What’s the Frequency, Kenneth?: Sublinear Fourier Sampling Off the Grid (2015) (41)
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach (2017) (41)
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints (2019) (41)
Faster Coordinate Descent via Adaptive Importance Sampling (2017) (41)
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator (2019) (40)
Acoustic Multitarget Tracking Using Direction-of-Arrival Batches (2007) (39)
Robust Maximization of Non-Submodular Objectives (2018) (38)
General direction-of-arrival tracking with acoustic nodes (2005) (37)
Fast and Provable ADMM for Learning with Generative Priors (2019) (36)
Stochastic Spectral Descent for Restricted Boltzmann Machines (2015) (36)
Filtered Variation method for denoising and sparse signal processing (2012) (35)
Constrained convex minimization via model-based excessive gap (2014) (35)
An SVD-free Approach to a Class of Structured Low Rank Matrix Optimization Problems with Application to System Identification (2013) (35)
Dynamic sparse state estimation using ℓ1-ℓ1 minimization: Adaptive-rate measurement bounds, algorithms and applications (2015) (34)
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics (2020) (34)
Group-Sparse Model Selection: Hardness and Relaxations (2013) (34)
MATRIX ALPS: Accelerated low rank and sparse matrix reconstruction (2012) (33)
Model-based sparse component analysis for reverberant speech localization (2014) (33)
An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization (2013) (33)
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization (2019) (33)
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming (2018) (31)
Randomized single-view algorithms for low-rank matrix approximation (2016) (31)
Frank-Wolfe works for non-Lipschitz continuous gradient objectives: Scalable poisson phase retrieval (2016) (30)
Bilinear Generalized Approximate Message Passing (2013) (30)
An ALPS view of sparse recovery (2011) (29)
A totally unimodular view of structured sparsity (2014) (29)
Stochastic Three-Composite Convex Minimization (2017) (28)
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization (2017) (28)
A new regret analysis for Adam-type algorithms (2020) (28)
On accelerated hard thresholding methods for sparse approximation (2011) (28)
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions (2013) (28)
Adaptive-Rate Reconstruction of Time-Varying Signals With Application in Compressive Foreground Extraction (2016) (28)
Structured Sparsity: Discrete and Convex approaches (2015) (27)
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity (2019) (27)
Learning Low-Dimensional Signal Models (2011) (27)
An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation (2019) (26)
Acoustic node calibration using a moving source (2006) (26)
A Reflected Forward-Backward Splitting Method for Monotone Inclusions Involving Lipschitzian Operators (2019) (26)
Active Learning of Multi-Index Function Models (2012) (25)
Stochastic Spectral Descent for Discrete Graphical Models (2016) (25)
What’s the Frequency, Kenneth?: Sublinear Fourier Sampling Off the Grid (2014) (24)
Model-based compressive sensing for multi-party distant speech recognition (2011) (24)
Preconditioned Spectral Descent for Deep Learning (2015) (24)
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI (2019) (23)
A Range-Only Multiple Target Particle Filter Tracker (2006) (23)
Recovery of compressible signals in unions of subspaces (2009) (22)
Multi-Party Speech Recovery Exploiting Structured Sparsity Models (2011) (22)
Randomized Low-Memory Singular Value Projection (2013) (22)
On the linear convergence of the stochastic gradient method with constant step-size (2017) (21)
Almost surely constrained convex optimization (2019) (21)
Converse bounds for noisy group testing with arbitrary measurement matrices (2016) (20)
Acoustic sensor network design for position estimation (2009) (20)
A Conditional-Gradient-Based Augmented Lagrangian Framework (2019) (20)
Near-Optimal Noisy Group Testing via Separate Decoding of Items (2018) (20)
Subquadratic Overparameterization for Shallow Neural Networks (2021) (19)
On the Convergence of Stochastic Primal-Dual Hybrid Gradient (2019) (19)
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems (2023) (19)
Fast initialization of particle filters using a modified metropolis-Hastings algorithm: mode-hungry approach (2004) (19)
On Certifying Non-uniform Bound against Adversarial Attacks (2019) (18)
How little does non-exact recovery help in group testing? (2017) (18)
DiGress: Discrete Denoising diffusion for graph generation (2022) (18)
Sparsistency of 1-Regularized M-Estimators (2015) (18)
Model-based Sketching and Recovery with Expanders (2014) (18)
Gaussian Approximations for Energy-Based Detection and Localization in Sensor Networks (2007) (18)
Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost (2015) (17)
Convex Optimization for Big Data (2014) (17)
Stochastic Three-Composite Convex Minimization with a Linear Operator (2018) (17)
Joint Acoustic-Video Fingerprinting of Vehicles, Part I (2007) (17)
Signal Recovery and System Calibration from Multiple Compressive Poisson Measurements (2015) (16)
Phase Transitions in the Pooled Data Problem (2017) (16)
An adaptive primal-dual framework for nonsmooth convex minimization (2018) (16)
Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization (2016) (16)
An adaptive sublinear-time block sparse fourier transform (2017) (15)
STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization (2021) (15)
Composite Convex Minimization Involving Self-concordant-Like Cost Functions (2015) (15)
Computational methods for underdetermined convolutive speech localization and separation via model-based sparse component analysis (2016) (15)
Stochastic Frank-Wolfe for Composite Convex Minimization (2019) (15)
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods (2018) (15)
Let's be honest: An optimal no-regret framework for zero-sum games (2018) (15)
Compressive sensing for sensor calibration (2008) (14)
An Eight-Lane 7-Gb/s/pin Source Synchronous Single-Ended RX With Equalization and Far-End Crosstalk Cancellation for Backplane Channels (2018) (14)
Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants (2018) (14)
Iterative Classroom Teaching (2018) (14)
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms (2018) (13)
Combinatorial Penalties: Which structures are preserved by convex relaxations? (2017) (13)
An Efficient Streaming Algorithm for the Submodular Cover Problem (2016) (13)
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral-Regularization Algorithms (2018) (13)
Energy-aware adaptive bi-Lipschitz embeddings (2013) (13)
Score matching enables causal discovery of nonlinear additive noise models (2022) (13)
Random extrapolation for primal-dual coordinate descent (2020) (13)
Rethinking Sampling in Parallel MRI: A Data-Driven Approach (2019) (13)
Time-Data Tradeoffs by Aggressive Smoothing (2014) (13)
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize (2022) (13)
Computational methods for structured sparse component analysis of convolutive speech mixtures (2012) (13)
A Bayesian Framework for Target Tracking using Acoustic and Image Measurements (2005) (12)
On low-power analog implementation of particle filters for target tracking (2006) (12)
A game theoretic approach to expander-based compressive sensing (2011) (12)
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction (2015) (12)
Scalable Sparse Covariance Estimation via Self-Concordance (2014) (12)
Optimal Maneuvering of Seismic Sensors for Localization of Subsurface Targets (2007) (12)
Scalable convex methods for phase retrieval (2015) (12)
Machine Learning From Distributed, Streaming Data [From the Guest Editors] (2020) (11)
Hard thresholding with norm constraints (2012) (11)
On the Difficulty of Selecting Ising Models With Approximate Recovery (2016) (11)
Smoothing technique for nonsmooth composite minimization with linear operator (2017) (11)
COMPRESSIVE WIRELESS ARRAYS FOR BEARING ESTIMATION OF SPARSE SOURCES IN ANGLE DOMAIN (2007) (11)
A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization (2018) (11)
Convergence of the Exponentiated Gradient Method with Armijo Line Search (2017) (11)
Fast hard thresholding with Nesterov"s gradient method (2010) (11)
VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS (2004) (11)
Estimating Target State Distributions In a Distributed Sensor Network Using a Monte-Carlo Approach (2005) (10)
Learning ridge functions with randomized sampling in high dimensions (2012) (10)
A Multi Target Bearing Tracking System using Random Sampling Consensus (2007) (10)
A Natural Actor-Critic Framework for Zero-Sum Markov Games (2022) (10)
An acoustic multiple target tracker (2005) (10)
Tracking of multiple wideband targets using passive sensor arrays and particle filters (2002) (9)
Lower Bounds on Active Learning for Graphical Model Selection (2016) (9)
Regret minimization in stochastic non-convex learning via a proximal-gradient approach (2020) (9)
Distributed bearing estimation via matrix completion (2010) (9)
Joint Acoustic-Video Fingerprinting of Vehicles, Part II (2007) (8)
A single-phase, proximal path-following framework (2016) (8)
Consistency of ℓ1-regularized maximum-likelihood for compressive Poisson regression (2015) (8)
Wavelet packet best basis search using generalized Renyi entropy (2002) (8)
Proposal strategies for joint state-space tracking with particle filters (2005) (8)
A 5.9mW/Gb/s 7Gb/s/pin 8-lane single-ended RX with crosstalk cancellation scheme using a XCTLE and 56-tap XDFE in 32nm SOI CMOS (2015) (8)
Metric learning with rank and sparsity constraints (2014) (8)
Stochastic Forward Douglas-Rachford Splitting for Monotone Inclusions (2016) (8)
2-D sensor position perturbation analysis: equivalence to AWGN on array outputs (2002) (8)
A Reflected Forward-Backward Splitting Method for Monotone Inclusions Involving Lipschitzian Operators (2020) (8)
Tractability of interpretability via selection of group-sparse models (2013) (8)
Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch (2020) (8)
Convergence of adaptive algorithms for weakly convex constrained optimization (2020) (8)
A Newton Frank–Wolfe method for constrained self-concordant minimization (2020) (8)
Splitting the Smoothed Primal-Dual Gap: Optimal Alternating Direction Methods (2015) (8)
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces (2018) (8)
Overlapping Multi-Bandit Best Arm Identification (2019) (7)
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation (2022) (7)
An AC-Coupled Wideband Neural Recording Front-End With Sub-1 mm2×fJ/conv-step Efficiency and 0.97 NEF (2020) (7)
Mixed-mode Implementation of Particle Filters (2007) (7)
Low computation and low latency algorithms for distributed sensor network initialization (2007) (7)
Structured sampling and recovery of iEEG signals (2015) (7)
A distributed algorithm for partitioned robust submodular maximization (2017) (7)
A Learning-Based Framework for Quantized Compressed Sensing (2019) (7)
Compressive sensing meets game theory (2011) (7)
Pareto Frontiers of Sensor Networks for Localization (2008) (7)
Forward-reflected-backward method with variance reduction (2021) (7)
Sublinear Time, Approximate Model-based Sparse Recovery For All (2012) (7)
A 5 . 9 mW / Gb / s 7 Gb / s / pin 8-Lane Single-Ended RX with Crosstalk Cancellation Scheme using a XCTLE and 56-tap XDFE in 32 nm SOI CMOS (2016) (7)
Compressible Priors for High-dimensional Statistics (2011) (6)
A Plug-and-Play Deep Image Prior (2021) (6)
Implementation of Batch-Based Particle Filters for Multi-Sensor Tracking (2007) (6)
An optimal first-order primal-dual gap reduction framework for constrained convex optimization (2015) (6)
Universal Primal-Dual Proximal-Gradient Methods (2015) (6)
{Sparsistency of \ell_1-Regularized M-Estimators} (2014) (5)
Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings (2012) (5)
Learning-based near-optimal area-power trade-offs in hardware design for neural signal acquisition (2016) (5)
Optimal Rates for Spectral-regularized Algorithms with Least-Squares Regression over Hilbert Spaces (2018) (5)
Design Considerations for a Heterogeneous Network of Bearings-only Sensors using Sensor Management (2007) (5)
Controlling the Complexity and Lipschitz Constant improves polynomial nets (2022) (5)
A Geometric View on Constrained M-Estimators (2015) (5)
A first-order primal-dual method with adaptivity to local smoothness (2021) (5)
Learning in games from a stochastic approximation viewpoint (2022) (5)
Virtual-SMLM, a virtual environment for real-time interactive SMLM acquisition (2020) (5)
Active learning of self-concordant like multi-index functions (2015) (4)
Efficient and Near-Optimal Noisy Group Testing: An Information-Theoretic Framework (2017) (4)
Combinatorial Penalties: Structure preserved by convex relaxations (2017) (4)
Smooth Alternating Direction Methods for Nonsmooth Constrained Convex Optimization (2015) (4)
Learning data triage: Linear decoding works for compressive MRI (2016) (4)
To convexify or not? Regression with clustering penalties on graphs (2013) (4)
The Spectral Bias of Polynomial Neural Networks (2022) (4)
DCT Learning-Based Hardware Design for Neural Signal Acquisition Systems (2017) (4)
Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections (2020) (4)
Near-Optimal Noisy Group Testing via Separate Decoding of Items (2018) (4)
Multi target direction-of-arrival tracking using road priors (2006) (4)
Closed loop deep Bayesian inversion: Uncertainty driven acquisition for fast MRI (2019) (4)
Double-Loop Unadjusted Langevin Algorithm (2020) (4)
Fast proximal algorithms for Self-concordant function minimization with application to sparse graph selection (2013) (4)
General Proximal Gradient Method: A Case for Non-Euclidean Norms (2017) (3)
Supplementary Material Adversarially Robust Optimization with Gaussian Processes (2018) (3)
Randomized Singular Value Projection (2013) (3)
Structured sparse coding for microphone array location calibration (2012) (3)
Approximate distributions for compressible signals (2009) (3)
Conditional gradient methods for stochastically constrained convex minimization (2020) (3)
The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers (2021) (3)
On the Complexity of a Practical Primal-Dual Coordinate Method (2022) (3)
Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks (2007) (3)
Real-Time DCT Learning-based Reconstruction of Neural Signals (2018) (3)
A Tutorial on Sparse Signal Acquisition and Recovery with Graphical Models (2010) (3)
Optimal Experiments With Seismic Sensors (2006) (3)
Partial recovery bounds for the sparse stochastic block model (2016) (2)
Acoustic node calibration using helicopter sounds and Monte-Carlo Markov chain methods (2004) (2)
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning (2022) (2)
A Monte-Carlo Approach for Tracking Mobile Personnel (2007) (2)
A joint radar-acoustic particle filter tracker with acoustic propagation delay compensation (2006) (2)
Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices (2016) (2)
Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration (2022) (2)
On the Double Descent of Random Features Models Trained with SGD (2021) (2)
A 16-Channel Wireless Neural Recording System-on-Chip with CHT Feature Extraction Processor in 65nm CMOS (2021) (2)
Hyperstereo algorithms for the perception of terrain drop-offs (2007) (2)
On the linear convergence of the projected stochastic gradient method with constant step-size (2017) (2)
Linear Inverse Problems with Norm and Sparsity Constraints (2015) (2)
Convergence of adaptive algorithms for constrained weakly convex optimization (2021) (2)
Sparse group covers and greedy tree approximations (2015) (2)
Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization (2018) (2)
A General Convergence Result for Mirror Descent with Armijo Line Search (2018) (2)
Introduction to the issue on signal processing for big data (2015) (2)
Barrier smoothing for nonsmooth convex minimization (2014) (2)
Manifold sparse beamforming (2013) (2)
Interaction-limited Inverse Reinforcement Learning (2020) (2)
Environment Shaping in Reinforcement Learning using State Abstraction (2020) (2)
A Monte-Carlo Method for Initializing Distributed Tracking Algorithms (2006) (2)
Stochastic Frank-Wolfe for Composite Convex Minimization (2022) (2)
Online performance guarantees for sparse recovery (2011) (2)
On the linear convergence of the stochastic gradient method with constant step-size (2018) (2)
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks (2020) (1)
1 A Bayesian Approach to Learning Low-Dimensional Signal Models from Incomplete Measurements 1 (2010) (1)
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization (2022) (1)
Room Acoustic Modeling Exploiting Joint Sparsity and Low-rank Structures (2013) (1)
Convexity in Source Separation (2014) (1)
Vehicle Fingerprinting Using Drive-By Sounds (2006) (1)
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration (2022) (1)
Estimation error of the constrained lasso (2016) (1)
Equivalence of synthesis and atomic formulations of sparse recovery (2012) (1)
Dimension-free Information Concentration via Exp-Concavity (2018) (1)
Nearly Minimal Over-Parametrization of Shallow Neural Networks (2019) (1)
Adversarial Audio Synthesis with Complex-valued Polynomial Networks (2022) (1)
Convex Block-sparse Linear Regression with Expanders - Provably (2016) (1)
Convergence Analysis for Sequential Monte Carlo Receivers in Communications Applications (2006) (1)
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization (2021) (1)
Estimation Error of the Lasso (2016) (1)
Map estimation for Bayesian mixture models with submodular priors (2014) (1)
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) (2022) (1)
Sound and Complete Verification of Polynomial Networks (2022) (1)
Efficient learning of smooth probability functions from Bernoulli tests with guarantees (2018) (1)
Solving stochastic weak Minty variational inequalities without increasing batch size (2023) (1)
A General Convergence Result for the Exponentiated Gradient Method (2017) (1)
Chemical machine learning with kernels: The impact of loss functions (2019) (1)
Sparse simplex projections for portfolio optimization (2013) (1)
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study (2022) (1)
Proximal Point Imitation Learning (2022) (1)
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods (2022) (1)
Kernel Conjugate Gradient Methods with Random Projections (2018) (1)
Limits on Support Recovery with Probabilistic Models: An Information-Spectrum Approach (2015) (1)
A concentration-of-measure inequality for multiple-measurement models (2015) (1)
Structured Sparse Acoustic Modeling for Speech Separation (2013) (1)
Generalization Properties of NAS under Activation and Skip Connection Search (2022) (1)
Preconditioned Spectral Descent for Deep Learning : Supplemental Material (2015) (0)
Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces (2019) (0)
Appendix for “Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games” (2018) (0)
Matrix Recipes for Hard Thresholding Methods (2013) (0)
Programme on “ Modern Maximal Monotone Operator Theory : From Nonsmooth Optimization to Differential Inclusions ” January 28 – March 8 , 2019 organized (2019) (0)
Convergence of the Exponentiated Gradient Method with Armijo Line Search (2018) (0)
Regularization of polynomial networks for image recognition (2023) (0)
Revisiting adversarial training for the worst-performing class (2023) (0)
Inertial Three-Operator Splitting Method and Applications. (2019) (0)
Provable benefits of general coverage conditions in efficient online RL with function approximation (2023) (0)
A Multipath Sparse Beamfroming Method (2013) (0)
Factorized variational approximations for acoustic multi source localization (2008) (0)
IT ] 1 F eb 2 01 6 LEARNING DATA TRIAGE : LINEAR DECODING WORKS FOR COMPRESSIV E MRI (2021) (0)
( A review of a broad set of sparse models, analysis tools, and recovery algorithms within the graphical models formalism ) (2010) (0)
Energy-aware adaptive Johnson-Lindenstrauss embedding via RIP-based designs (2013) (0)
Scalable Semidefinite Programming with Linear Minimization Oracles (2019) (0)
Motivation and the Kullback - (2008) (0)
Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps (2023) (0)
Computational Methods for Convolutive Speech Localization and Separation via Model-based Sparse Component Analysis (2015) (0)
Self-Supervised Neural Architecture Search for Imbalanced Datasets (2021) (0)
Uncertainty-Driven Adaptive Sampling via GANs (2020) (0)
Matrix Recipes for Hard Thresholding Methods Report Title (2012) (0)
Efficient proximal mapping of the path-norm regularizer of shallow networks (2020) (0)
An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems (2018) (0)
WORKS VIA SPARSE POLYNOMIAL OPTIMIZATION (2020) (0)
Supplementary Material for “ Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices ” ( AISTATS 2016 (2016) (0)
A primal-dual framework for mixtures of regularizers (2015) (0)
A 16-Channel Neural Recording System-on-Chip With CHT Feature Extraction Processor in 65-nm CMOS (2022) (0)
Supplementary Material for “ Time-Varying Gaussian Process Bandit Optimization ” ( AISTATS 2016 ; (2016) (0)
Time – Data Tradeo ff s by Aggressive Smoothing (2014) (0)
Scalable Convex Methods for Low-Rank Matrix Problems (2017) (0)

This paper list is powered by the following services:

What Schools Are Affiliated With Volkan Cevher?

Volkan Cevher is affiliated with the following schools:

Volkan Cevher's Academic­Influence.com Rankings

Why Is Volkan Cevher Influential?

Volkan Cevher's Published Works

Published Works

What Schools Are Affiliated With Volkan Cevher?

Volkan Cevher's AcademicInfluence.com Rankings