Publications

• Y. M. Lu, M. I. Letey, J. A. Zavatone-Veth, A. Maiti, and C. Pehlevan
  Asymptotic theory of in-context learning by linear attention
  Proceedings of the National Academy of Sciences (PNAS), vol. 122, no. 28 (2025).
  [Asymptotic theory of in-context learning by linear attentionPublisher] [Asymptotic theory of in-context learning by linear attention arXiv]
• Y. M. Lu and H.-T. Yau
  An equivalence principle for the spectrum of random inner-product kernel matrices
  Annals of Applied Probability, 35(4): 2411-2470 (2025).
  [An equivalence principle for the spectrum of random inner-product kernel matricesPublisher] [An Equivalence Principle for the Spectrum of Random Inner-Product Kernel Matrices arXiv]
• H. Cui, C. Pehlevan, and Y. M. Lu
  A solvable model of learning generative diffusion: theory and insights
  Conference on Neural Information Processing Systems (NeurIPS) (2025).
  [A precise asymptotic analysis of learning diffusion models: theory and insights arXiv]
• N. Barnfield, H. Cui, and Y. M. Lu
  High-Dimensional analysis of single-layer attention for sparse-token classification
  Preprint (2025). [High-Dimensional analysis of single-layer attention for sparse-token classification arXiv]
• M. I. Letey, J. A. Zavatone-Veth, Y. M. Lu, and C. Pehlevan
  Pretrain-Test Task Alignment Governs Generalization in In-Context Learning
  Preprint (2025). [Pretrain-Test Task Alignment Governs Generalization in In-Context Learning arXiv]
• Z. Fan, J. Ko, B. Loureiro, Y. M. Lu, and Y. Shen
  Dynamical mean-field analysis of adaptive Langevin diffusions: Propagation-of-chaos and convergence of the linear response
  Preprint (2025). [Dynamical mean-field analysis of adaptive Langevin diffusions: Propagation-of-chaos and convergence of the linear responsearXiv]
• Z. Fan, J. Ko, B. Loureiro, Y. M. Lu, and Y. Shen
  Dynamical mean-field analysis of adaptive Langevin diffusions: Replica-symmetric fixed point and empirical Bayes
  Preprint (2025). [Dynamical mean-field analysis of adaptive Langevin diffusions: Replica-symmetric fixed point and empirical BayesarXiv]
• S. Dubova, Y. M. Lu, B. McKenna, and H.-T. Yau
  Universality for the global spectrum of random inner-product kernel matrices in the polynomial regime
  Annals of Applied Probability, under minor revision (2025).
  [Universality for the global spectrum of random inner-product kernel matrices in the polynomial regimearXiv]
• Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, and Bruno Loureiro
  A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
  The 28th International Conference on Artificial Intelligence and Statistics (AISTATS) (2025). (Oral paper)
  [A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization CapabilitiesarXiv]
• R. Dudeja, S. Sen, and Y. M. Lu
  Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing Matrices
  IEEE Transactions on Information Theory, vol. 70, no. 11 (2024).
  [Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing MatricesPublisher] [Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing MatricesarXiv]
• B. Cakmak, Y. M. Lu, and M. Opper
  A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification
  IEEE International Symposium on Information Theory (ISIT) (2024).
  [A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class ClassificationarXiv]
• H. Hu, Y. M. Lu, and T. Misiakiewicz
  Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
  Preprint (2024). [Asymptotics of Random Feature Regression Beyond the Linear Scaling RegimearXiv]
• Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, and Bruno Loureiro
  Asymptotics of feature learning in two-layer networks after one gradient-step
  International Conference on Machine Learning (ICML), (2024). (spotlight paper)
  [Asymptotics of feature learning in two-layer networks after one gradient-steparXiv]
• L. Xiao, H. Hu, T. Misiakiewicz, Y. M. Lu, and J. Pennington
  Precise learning curves and higher-order scaling limits for dot-product kernel regression
  Journal of Statistical Mechanics: Theory and Experiment (2023).
  [Precise learning curves and higher-order scaling limits for dot-product kernel regressionPublisher] [Precise learning curves and higher-order scaling limits for dot-product kernel regressionarXiv]
• R. Dudeja, Y. M. Lu, and S. Sen
  Universality of Approximate Message Passing with Semi-Random Matrices
  Annals of Probability, 51(5): 1616-1683 (2023).
  [Universality of Approximate Message Passing with Semi-Random MatricesPublisher] [Universality of Approximate Message Passing with Semi-Random MatricesarXiv]
• H. Hu and Y. M. Lu
  Universality Laws for High-Dimensional Learning with Random Features
  IEEE Transactions on Information Theory, vol. 69, no. 3 (2023).
  [Universality Laws for High-Dimensional Learning with Random FeaturesPublisher] [Universality Laws for High-Dimensional Learning with Random FeaturesarXiv]
• H. Hu and Y. M. Lu
  SLOPE for Sparse Linear Regression: Asymptotics and Optimal Regularization
  IEEE Transactions on Information Theory, vol. 68, no. 11, pp. 7627–7664 (2022).
  [SLOPE for Sparse Linear Regression: Asymptotics and Optimal RegularizationPublisher] [SLOPE for Sparse Linear Regression: Asymptotics and Optimal RegularizationarXiv]
• L. Xiao, H. Hu, T. Misiakiewicz, Y. M. Lu, and J. Pennington
  Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression
  Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS) (2022).
  [Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel RegressionarXiv]
• H. Hu and Y. M. Lu
  Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
  Technical Report (2022). [Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear RegimearXiv]
• B. Çakmak, Y. M. Lu, and M. Opper
  Analysis of Random Sequential Message Passing Algorithms for Approximate Inference
  Journal of Statistical Mechanics: Theory and Experiment, no. 073401 (2022).
  [Analysis of Random Sequential Message Passing Algorithms for Approximate InferencePublisher] [Analysis of Random Sequential Message Passing Algorithms for Approximate InferencearXiv]
• Y. M. Lu
  Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal Ensembles
  IEEE Transactions on Information Theory, vol. 67, no. 12, pp. 8264–8272 (2021).
  [Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal EnsemblesPublisher] [Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal EnsemblesarXiv]
• O. Dhifallah and Y. M. Lu
  A Precise Performance Analysis of Learning with Random Features
  Technical Report (2021). [A Precise Performance Analysis of Learning with Random FeaturesarXiv]
• A. Maillard, F. Krzakala, Y. M. Lu, and L. Zdeborová
  Construction of optimal spectral methods in phase retrieval
  Mathematical and Scientific Machine Learning (2021).
  [Construction of optimal spectral methods in phase retrievalarXiv]
• O. Dhifallah and Y. M. Lu
  On the Inherent Regularization Effects of Noise Injection During Training
  International Conference on Machine Learning (ICML) (2021).
  [On the Inherent Regularization Effects of Noise Injection During TrainingarXiv]
• O. Dhifallah and Y. M. Lu
  Phase Transitions in Transfer Learning for High-Dimensional Perceptrons
  Entropy (Special Issue: The Role of Signal Processing and Information Theory in Modern Machine Learning), vol. 23, no. 4 (2021).
  [Phase Transitions in Transfer Learning for High-Dimensional PerceptronsPublisher] [Phase Transitions in Transfer Learning for High-Dimensional PerceptronsarXiv]
• H. Hu and Y. M. Lu
  The Limiting Poisson Law of Massive MIMO Detection with Box Relaxation
  IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 3, pp. 695–704 (2020).
  [The Limiting Poisson Law of Massive MIMO Detection with Box RelaxationPublisher] [The Limiting Poisson Law of Massive MIMO Detection with Box RelaxationarXiv]
• Y. M. Lu and G. Li
  Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation
  Information and Inference: A Journal of the IMA, vol. 9, no. 3, pp. 507–541 (2020).
  [Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex EstimationPublisher] [Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex EstimationarXiv]
• B. Aubin, Y. M. Lu, F. Krzakala, and L. Zdeborová
  Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization
  Conference on Neural Information Processing Systems (NeurIPS) (2020).
  [Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimizationarXiv]
• F. Mignacco, F. Krzakala, Y. M. Lu, and L. Zdeborová
  The role of regularization in classification of high-dimensional noisy Gaussian mixture
  International Conference on Machine Learning (ICML) (2020).
  [The role of regularization in classification of high-dimensional noisy Gaussian mixturearXiv]
• W. Luo, W. Alghamdi, and Y. M. Lu
  Optimal Spectral Initialization for Signal Recovery with Applications to Phase Retrieval
  IEEE Transactions on Signal Processing, vol. 67, no. 9, pp. 2347–2356 (2019).
  [Optimal Spectral Initialization for Signal Recovery with Applications to Phase RetrievalPublisher] [Optimal Spectral Initialization for Signal Recovery with Applications to Phase RetrievalarXiv]
• Y. Chi, Y. M. Lu, and Y. Chen
  Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
  IEEE Transactions on Signal Processing, vol. 67, no. 20, pp. 5239–5269 (2019).
  [Nonconvex Optimization Meets Low-Rank Matrix Factorization: An OverviewPublisher] [Nonconvex Optimization Meets Low-Rank Matrix Factorization: An OverviewarXiv]
• C. Wang and Y. M. Lu
  The scaling limit of high-dimensional online independent component analysis
  Journal of Statistical Mechanics (Special Issue on Machine Learning), vol. 2019 (2019).
  [The scaling limit of high-dimensional online independent component analysisPublisher]
• C. Wang, H. Hu, and Y. M. Lu
  A Solvable High-Dimensional Model of GAN
  Thirty-third Conference on Neural Information Processing Systems (NeurIPS) (2019).
  [A Solvable High-Dimensional Model of GANarXiv]
• L. Saglietti, Y. M. Lu, and C. Lucibello
  Generalized Approximate Survey Propagation for High-Dimensional Estimation
  International Conference on Machine Learning (ICML) (2019).
  [Generalized Approximate Survey Propagation for High-Dimensional EstimationarXiv]
• H. Hong and Y. M. Lu
  Asymptotics and optimal designs of SLOPE for sparse linear regression
  IEEE International Symposium on Information Theory (ISIT) (2019).
  [Asymptotics and optimal designs of SLOPE for sparse linear regressionPublisher]
• D. Simon, J. Sulam, Y. Romano, Y. M. Lu, and M. Elad
  MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance
  IEEE Transactions on Signal Processing, vol. 67, no. 17 (2019).
  [MMSE Approximation For Sparse Coding Algorithms Using Stochastic ResonancePublisher] [MMSE Approximation For Sparse Coding Algorithms Using Stochastic ResonancearXiv]
• O. Dhifallah, C. Thrampoulidis, and Y. M. Lu
  Phase Retrieval via Polytope Optimization: Geometry, Phase Transitions, and New Algorithms
  Technical Report (2018). [Phase Retrieval via Polytope Optimization: Geometry, Phase Transitions, and New AlgorithmsarXiv]
• C. Wang, Y. C. Eldar, and Y. M. Lu
  Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis
  IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 6 (2018).
  [Subspace Estimation from Incomplete Observations: A High-Dimensional AnalysisPublisher] [Subspace Estimation from Incomplete Observations: A High-Dimensional AnalysisarXiv]
• Y. M. Lu, J. Oñativia, and P. L. Dragotti
  Sparse Representation in Fourier and Local Bases Using ProSparse: A Probabilistic Analysis
  IEEE Transactions on Information Theory, vol. 64, no. 4, pp. 2639–2647 (2018).
  [Sparse Representation in Fourier and Local Bases Using ProSparse: A Probabilistic AnalysisPublisher] [Sparse Representation in Fourier and Local Bases Using ProSparse: A Probabilistic AnalysisarXiv]
• L. Balzano, Y. Chi, and Y. M. Lu
  A Modern Perspective on Streaming PCA and Subspace Tracking: The Missing Data Case
  Proceedings of the IEEE, vol. 106, no. 8, pp. 1293–1310 (2018).
  [A Modern Perspective on Streaming PCA and Subspace Tracking: The Missing Data CasePublisher] [A Modern Perspective on Streaming PCA and Subspace Tracking: The Missing Data CasePDF]
• C. Wang, J. Mattingly, and Y. M. Lu
  Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCA
  Technical Report (2017). [Scaling Limit: Exact and Tractable Analysis of Online Learning Algorithms with Applications to Regularized Regression and PCAarXiv]
• O. Dhifallah, C. Thrampoulidis, and Y. M. Lu
  Phase Retrieval via Linear Programming: Fundamental Limits and Algorithmic Improvements
  55th Annual Allerton Conference on Communication, Control, and Computing (2017).
  [Phase Retrieval via Linear Programming: Fundamental Limits and Algorithmic ImprovementsarXiv]
• C. Wang and Y. M. Lu
  The Scaling Limit of High-Dimensional Online Independent Component Analysis
  Conference on Neural Information Processing Systems (NIPS), (2017). (Spotlight paper)
  [The Scaling Limit of High-Dimensional Online Independent Component AnalysisPDF]
• O. Dhifallah and Y. M. Lu
  Fundamental Limits of PhaseMax for Phase Retrieval: A Replica Analysis
  7th IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2017).
  Best Student Paper Award (First Prize)
  [Fundamental Limits of PhaseMax for Phase Retrieval: A Replica AnalysisPublisher] [Fundamental Limits of PhaseMax for Phase Retrieval: A Replica AnalysisarXiv]
  
• Y. M. Lu and G. Li
  Spectral Initialization for Nonconvex Estimation: High-Dimensional Limit and Phase Transitions
  IEEE International Symposium on Information Theory (ISIT) (2017).
  [Spectral Initialization for Nonconvex Estimation: High-Dimensional Limit and Phase TransitionsPublisher] [Spectral Initialization for Nonconvex Estimation: High-Dimensional Limit and Phase TransitionsPDF]
• C. Wang, Y. Eldar, and Y. M. Lu
  Subspace Estimation from Incomplete Observations: A Precise High-Dimensional Analysis
  Signal Processing with Adaptive Structured Representatives (SPARS) Workshop (2017).
  [Subspace Estimation from Incomplete Observations: A Precise High-Dimensional AnalysisPDF]
• S. H. Chan, T. Zickler, and Y. M. Lu
  Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective
  IEEE Transactions on Image Processing, vol. 26, no. 11, pp. 5107–5121 (2017).
  [Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model PerspectivePublisher] [Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model PerspectivearXiv]
• C. Wang and Y. M. Lu
  Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase Transitions
  IEEE Information Theory Workshop (ITW) (2016).
  [Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and Phase TransitionsPDF]
• Y. Chi and Y. M. Lu
  Kaczmarz Method for Solving Quadratic Equations
  IEEE Signal Processing Letters, vol. 23, no. 9, pp. 1183–1187 (2016).
  [Kaczmarz Method for Solving Quadratic EquationsPublisher] [Kaczmarz Method for Solving Quadratic EquationsPDF]
• G. Li, Y. Gu, and Y. M. Lu
  Phase Retrieval Using Iterative Projections: Dynamics in the Large Systems Limit
  Allerton Conference on Communications, Control, and Computing (2015).
  [Phase Retrieval Using Iterative Projections: Dynamics in the Large Systems LimitPDF]
• S. H. Chan, T. Zickler, and Y. M. Lu
  Understanding symmetric smoothing filters via Gaussian mixtures
  IEEE International Conference on Image Processing (2015).
  [Understanding symmetric smoothing filters via Gaussian mixturesPublisher]
• A. Agaskar and Y. M. Lu
  Optimal Detection of Random Walks on Graphs: Performance Analysis via Statistical Physics
  Technical Report (2015). [Optimal Detection of Random Walks on Graphs: Performance Analysis via Statistical PhysicsarXiv]
• A. Agaskar and Y. M. Lu
  Optimal hypothesis testing with combinatorial structure: Detecting random walks on graphs
  Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA (2014).
  [Optimal hypothesis testing with combinatorial structure: Detecting random walks on graphsPublisher]
• A. Agaskar, C. Wang, and Y. M. Lu
  Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilities
  IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA (2014).
  Best Student Paper Award
  [Randomized Kaczmarz algorithms: Exact MSE analysis and optimal sampling probabilitiesPDF]
  
• Y. M. Lu
  A Framework for Adaptive Parameter Estimation with Finite Memory
  IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, TX (2013).
  [A Framework for Adaptive Parameter Estimation with Finite MemoryPublisher]
• A. Agaskar and Y. M. Lu
  ALARM: A Logistic Auto-Regressive Model for binary processes on networks
  IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, TX (2013).
  [ALARM: A Logistic Auto-Regressive Model for binary processes on networksPublisher] [ALARM: A Logistic Auto-Regressive Model for binary processes on networksPDF]
• S. H. Chan, T. Zickler, and Y. M. Lu
  Fast non-local filtering by random sampling: It works, especially for large images
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada (2013).
  [Fast non-local filtering by random sampling: It works, especially for large imagesPublisher] [Fast non-local filtering by random sampling: It works, especially for large imagesPDF]
• A. Agaskar and Y. M. Lu
  Detecting random walks hidden in noise: Phase transition on large graphs
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada (2013).
  [Detecting random walks hidden in noise: Phase transition on large graphsPublisher] [Detecting random walks hidden in noise: Phase transition on large graphsPDF]

Earlier work (on signal representation, sampling, and image processing)

• G. Baechler, M. Kreković, J. Ranieri, A. Chebira, Y. M. Lu, and M. Vetterli
  Super resolution phase retrieval for sparse signals
  IEEE Transactions on Signal Processing, vol. 67, no. 18 (2019). [Super resolution phase retrieval for sparse signalsPublisher] [Super resolution phase retrieval for sparse signalsarXiv]
• R. Yin, T. Gao, Y. M. Lu, and I. Daubechies
  A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets
  SIAM Journal on Imaging Sciences, vol. 10, no. 2, pp. 711–750 (2017). [A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution FrameletsPublisher] [A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution FrameletsPDF]
• D. M. Merfeld, T. K. Clark, Y. M. Lu, and F. Karmali
  Dynamics of Individual Perceptual Decisions
  Journal of Neurophysiology, vol. 115, no. 1 (2016). [Dynamics of Individual Perceptual DecisionsPublisher]
• F. Sroubek, J. Kamenicky, and Y. M. Lu
  Decomposition space-variant blur in image deconvolution
  IEEE Signal Processing Letters, vol. 23, no. 3, pp. 346–350 (2016).
  [Decomposition space-variant blur in image deconvolutionPublisher] [Decomposition space-variant blur in image deconvolutionPDF]
• C. Hu, J. Sepulcre, K. A. Johnson, G. E. Fakhri, Y. M. Lu, and Q. Li
  Matched Signal Detection on Graphs: Theory and Application to Brain Imaging Data Classification
  NeuroImage, vol. 125, pp. 587–600 (2016). [Matched Signal Detection on Graphs: Theory and Application to Brain Imaging Data ClassificationPublisher] [Matched Signal Detection on Graphs: Theory and Application to Brain Imaging Data ClassificationPDF]
• I. Dokmanic and Y. M. Lu
  Sampling Sparse Signals on the Sphere: Algorithms and Applications
  IEEE Transactions on Signal Processing, vol. 64, no. 1, pp. 189–202 (2016). [Sampling Sparse Signals on the Sphere: Algorithms and ApplicationsPublisher] [Sampling Sparse Signals on the Sphere: Algorithms and ApplicationsarXiv]
• J. Oñativia, P. L. Dragotti, and Y. M. Lu
  ProSparse denoise: Prony’s based sparsity pattern recovery in the presence of noise
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2016). [ProSparse denoise: Prony’s based sparsity pattern recovery in the presence of noisePublisher]
• A. Minot, Y. M. Lu, and N. Li
  A Distributed Gauss-Newton Method for Power System State Estimation
  IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3804–3815 (2016). [A Distributed Gauss-Newton Method for Power System State EstimationPublisher]
• J. Oñativia, P. L. Dragotti, and Y. M. Lu
  Sparsity according to Prony, Average Performance Analysis
  Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, Cambridge, England (2015). [Sparsity according to Prony, Average Performance AnalysisPDF]
• C. Hu, et al.
  A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease
  PLOS ONE, vol. 10, no. 5 (2015). [A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s DiseasePublisher]
• S. H. Chan and Y. M. Lu
  Efficient image reconstruction for gigapixel quantum image sensors
  IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA (2014). [Efficient image reconstruction for gigapixel quantum image sensorsPublisher] [Efficient image reconstruction for gigapixel quantum image sensorsPDF]
• J. Oñativia, Y. M. Lu, and P. L. Dragotti
  Finite Dimensional FRI
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence (2014). [Finite Dimensional FRIPublisher] [Finite Dimensional FRIPDF]
• P. L. Dragotti and Y. M. Lu
  On Sparse Representation in Fourier and Local Bases
  IEEE Transactions on Information Theory, vol. 60, no. 12, pp. 7888–7899 (2014). [On Sparse Representation in Fourier and Local BasesPublisher] [On Sparse Representation in Fourier and Local BasesPDF]
• S. Maranò, D. Fäh, and Y. M. Lu
  Sensor Placement for the Analysis of Seismic Surface Waves: Source of Error, Design Criterion, and Array Design Algorithms
  Geophysical Journal International, vol. 197, no. 3, pp. 1566–1581 (2014). [Sensor Placement for the Analysis of Seismic Surface Waves: Source of Error, Design Criterion, and Array Design AlgorithmsPublisher]
• Z. Sadeghipoor, Y. M. Lu, and S. Susstrunk
  A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada (2013). [A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensorPublisher]
• I. Dokmanic, R. Parhizkar, A. Walther, Y. M. Lu, and M. Vetterli
  Acoustic Echoes Reveal Room Shape
  Proceedings of the National Academy of Sciences (PNAS), vol. 110, no. 30, pp. 12186–12191 (2013). [Acoustic Echoes Reveal Room ShapePublisher] [Acoustic Echoes Reveal Room ShapePDF]
• Y. M. Lu
  Adaptive sensing and inference for single-photon imaging
  47th Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD (2013). [Adaptive sensing and inference for single-photon imagingPublisher] [Adaptive sensing and inference for single-photon imagingPDF]
• C. Hu, L. Cheng, J. Sepulcre, G. E. Fakhri, Y. M. Lu, and Q. Li
  Matched signal detection on graphs: Theory and application to brain network classification
  23rd International Conference on Information Processing in Medical Imaging (IPMI), Asilomar, CA (2013). [Matched signal detection on graphs: Theory and application to brain network classificationPublisher] [Matched signal detection on graphs: Theory and application to brain network classificationPDF]
• A. Agaskar and Y. M. Lu
  A Spectral Graph Uncertainty Principle
  IEEE Transactions on Information Theory, vol. 59, no. 7, pp. 4338–4356 (2013). [A Spectral Graph Uncertainty PrinciplePublisher] [A Spectral Graph Uncertainty PrinciplePDF] [A Spectral Graph Uncertainty PrinciplearXiv]
• C. Hu, L. Cheng, J. Sepulcre, G. E. Fakhri, Y. M. Lu, and Q. Li
  A graph theoretical regression model for brain connectivity learning of Alzheimer’s disease
  International Symposium on Biomedical Imaging (ISBI), San Francisco, CA (2013). [A graph theoretical regression model for brain connectivity learning of Alzheimer’s diseasePublisher] [A graph theoretical regression model for brain connectivity learning of Alzheimer’s diseasePDF]
• C. Hu and Y. M. Lu
  Adaptive time-sequential binary sensing for high dynamic range imaging
  SPIE Conference on Advanced Photon Counting Techniques VI, Baltimore, MD (2012). [Adaptive time-sequential binary sensing for high dynamic range imagingPublisher]
• C. Hu, L. Cheng, J. Sepulcre, G. E. Fakhri, Y. M. Lu, and Q. Li
  Graph-Based regularization for color image demosaicking
  IEEE International Conference on Image Processing (ICIP), Orlando, FL (2012). [Graph-Based regularization for color image demosaickingPublisher] [Graph-Based regularization for color image demosaickingPDF]
• Z. Sadeghipoor, Y. M. Lu, and S. Süsstrunk
  Optimal spectral sensitivity functions for single sensor color imaging
  SPIE Conference on Digital Photography VIII, Burlingame (2012). [Optimal spectral sensitivity functions for single sensor color imagingPublisher]
• A. Agaskar and Y. M. Lu
  Uncertainty principles for signals defined on graphs: Bounds and characterizations
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto (2012). [Uncertainty principles for signals defined on graphs: Bounds and characterizationsPublisher] [Uncertainty principles for signals defined on graphs: Bounds and characterizationsarXiv]
• Y. Xiong and Y. M. Lu
  Blind estimation and low-rate sampling of sparse MIMO systems with common support
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto (2012). [Blind estimation and low-rate sampling of sparse MIMO systems with common supportPublisher] [Blind estimation and low-rate sampling of sparse MIMO systems with common supportPDF]
• M. N. Do and Y. M. Lu
  Multidimensional Filter Banks and Multiscale Geometric Representations
  Foundations and Trends in Signal Processing, vol. 5, no. 3, pp. 157–264 (2012). [Multidimensional Filter Banks and Multiscale Geometric RepresentationsPublisher] [Multidimensional Filter Banks and Multiscale Geometric RepresentationsPDF]
• F. Yang, Y. M. Lu, L. Sbaiz, and M. Vetterli
  Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics
  IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1421–1436 (2012). [Bits from Photons: Oversampled Image Acquisition Using Binary Poisson StatisticsPublisher] [Bits from Photons: Oversampled Image Acquisition Using Binary Poisson StatisticsarXiv]
• A. Agaskar and Y. M. Lu
  An uncertainty principle for functions defined on graphs
  SPIE Conference on Wavelets and Sparsity, San Diego (2011). [An uncertainty principle for functions defined on graphsPublisher] [An uncertainty principle for functions defined on graphsarXiv]
• Z. Sadeghipoor, Y. M. Lu, and S. Susstrunk
  Correlation-Based joint acquisition and demosaicing of visible and near-infrared images
  IEEE International Conference on Image Processing (ICIP), Brussels (2011). [Correlation-Based joint acquisition and demosaicing of visible and near-infrared imagesPublisher] [Correlation-Based joint acquisition and demosaicing of visible and near-infrared imagesPDF]
• Y. M. Lu and M. Vetterli
  Sparse spectral factorization: Unicity and reconstruction algorithms
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague (2011). [Sparse spectral factorization: Unicity and reconstruction algorithmsPublisher] [Sparse spectral factorization: Unicity and reconstruction algorithmsPDF]
• Y. M. Lu, P. L. Dragotti, and M. Vetterli
  Localizing point sources in diffusion fields from spatiotemporal measurements
  International Conference on Sampling Theory and Applications (SampTA), Singapore (2011). [Localizing point sources in diffusion fields from spatiotemporal measurementsPublisher] [Localizing point sources in diffusion fields from spatiotemporal measurementsPDF]
• J. Ranieri, A. Chebira, Y. M. Lu, and M. Vetterli
  Sampling and reconstructing diffusion fields with localized sources
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague (2011). [Sampling and reconstructing diffusion fields with localized sourcesPublisher] [Sampling and reconstructing diffusion fields with localized sourcesPDF]
• I. Dokmanić, Y. M. Lu, and M. Vetterli
  Can one hear the shape of a room: The 2-D polygonal case
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague (2011). [Can one hear the shape of a room: The 2-D polygonal casePublisher] [Can one hear the shape of a room: The 2-D polygonal casePDF]
  Best Student Paper Award (ICASSP 2011).
• M. McCormick, Y. M. Lu, and M. Vetterli
  Learning sparse systems at sub-Nyquist rates: A frequency-domain approach
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas (2010). [Learning sparse systems at sub-Nyquist rates: A frequency-domain approachPublisher] [Learning sparse systems at sub-Nyquist rates: A frequency-domain approachPDF]
• Y. M. Lu and M. Vetterli
  Multichannel sampling with unknown gains and offsets: A fast reconstruction algorithm
  Allerton Conference on Communication, Control and Computing, Monticello, IL (2010). [Multichannel sampling with unknown gains and offsets: A fast reconstruction algorithmPublisher] [Multichannel sampling with unknown gains and offsets: A fast reconstruction algorithmPDF]
• F. Yang, Y. M. Lu, L. Sbaiz, and M. Vetterli
  An optimal algorithm for reconstructing images from binary measurements
  SPIE Conference on Computational Imaging VIII, San Jose, CA (2010). [An optimal algorithm for reconstructing images from binary measurementsPublisher]
• A. Hormati, O. Roy, Y. M. Lu, and M. Vetterli
  Distributed Sampling of Correlated Signals Linked by Sparse Filtering: Theory and Applications
  IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1095–1109 (2010). [Distributed Sampling of Correlated Signals Linked by Sparse Filtering: Theory and ApplicationsPublisher] [Distributed Sampling of Correlated Signals Linked by Sparse Filtering: Theory and ApplicationsPDF]
• Y. M. Lu, M. Karzand, and M. Vetterli
  Demosaicking by Alternating Projections: Theory and Fast One-Step Implementation
  IEEE Transactions on Image Processing, vol. 19, no. 8, pp. 2085–2098 (2010). [Demosaicking by Alternating Projections: Theory and Fast One-Step ImplementationPublisher] [Demosaicking by Alternating Projections: Theory and Fast One-Step ImplementationPDF]
• Y. M. Lu, M. N. Do, and R. Laugesen
  Computable Fourier conditions for alias-free sampling and critical sampling
  International Conference on Sampling Theory and Applications (SampTA), Marseille, France (2009). [Computable Fourier conditions for alias-free sampling and critical samplingPublisher] [Computable Fourier conditions for alias-free sampling and critical samplingPDF]
• A. Hormati, O. Roy, Y. M. Lu, and M. Vetterli
  Distributed sensing of signals under a sparse filtering model
  International Conference on Sampling Theory and Applications (SampTA), Marseille, France (2009). [Distributed sensing of signals under a sparse filtering modelPublisher] [Distributed sensing of signals under a sparse filtering modelPDF]
• Y. M. Lu, C. Fredembach, M. Vetterli, and S. Süsstrunk
  Designing color filter arrays for the joint capture of visible and near-infrared images
  IEEE International Conference on Image Processing (ICIP), Cairo, Egypt (2009). [Designing color filter arrays for the joint capture of visible and near-infrared imagesPublisher] [Designing color filter arrays for the joint capture of visible and near-infrared imagesPDF]
• C. Carneiro, M. Karzand, F. Golay, Y. M. Lu, and M. Vetterli
  Assessment of digital surface models for the study of shadowing and radiation over the built environment using wireless sensor network data
  6th International Symposium on Spatial Data Quality, Newfoundland (2009). [Assessment of digital surface models for the study of shadowing and radiation over the built environment using wireless sensor network dataPublisher]
• Y. M. Lu and M. Vetterli
  Optimal color filter array design: Quantitative conditions and an efficient search procedure
  SPIE Electronic Imaging, Digital Photography V (2009). [Optimal color filter array design: Quantitative conditions and an efficient search procedurePublisher] [Optimal color filter array design: Quantitative conditions and an efficient search procedurePDF]
• Y. M. Lu, M. N. Do, and R. S. Laugesen
  A Computable Fourier Condition Generating Alias-Free Sampling Lattices
  IEEE Transactions on Signal Processing, vol. 57, no. 5, pp. 1768–1782 (2009). [A Computable Fourier Condition Generating Alias-Free Sampling LatticesPublisher] [A Computable Fourier Condition Generating Alias-Free Sampling LatticesPDF]
• Y. M. Lu and M. Vetterli
  Distributed spatio-temporal sampling of diffusion fields from sparse instantaneous sources
  3rd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba (2009). [Distributed spatio-temporal sampling of diffusion fields from sparse instantaneous sourcesPublisher] [Distributed spatio-temporal sampling of diffusion fields from sparse instantaneous sourcesPDF]
• Y. M. Lu and M. Vetterli
  Spatial super-resolution of a diffusion field by temporal oversampling in sensor networks
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taiwan (2009). [Spatial super-resolution of a diffusion field by temporal oversampling in sensor networksPublisher] [Spatial super-resolution of a diffusion field by temporal oversampling in sensor networksPDF]
• O. Roy, A. Hormati, Y. M. Lu, and M. Vetterli
  Distributed sensing of signals linked by sparse filtering
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei (2009). [Distributed sensing of signals linked by sparse filteringPublisher] [Distributed sensing of signals linked by sparse filteringPDF]
• Y. M. Lu, M. Karzand, and M. Vetterli
  Iterative demosaicking accelerated: Theory and fast noniterative implementations
  SPIE Conference on Computational Imaging VI, San Jose, CA (2009). [Iterative demosaicking accelerated: Theory and fast noniterative implementationsPublisher] [Iterative demosaicking accelerated: Theory and fast noniterative implementationsPDF]
• G. Barrenetxea, F. Ingelrest, Y. M. Lu, and M. Vetterli
  Assessing the challenges of environmental signal processing through the SensorScope project
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas (2008), pp. 5149–5152. [Assessing the challenges of environmental signal processing through the SensorScope projectPublisher] [Assessing the challenges of environmental signal processing through the SensorScope projectPDF]
• Y. M. Lu and M. N. Do
  A Mapping-Based Design for Nonsubsampled Hourglass Filter Banks in Arbitrary Dimensions
  IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 1466–1478 (2008). [A Mapping-Based Design for Nonsubsampled Hourglass Filter Banks in Arbitrary DimensionsPublisher] [A Mapping-Based Design for Nonsubsampled Hourglass Filter Banks in Arbitrary DimensionsPDF]
• Y. M. Lu and M. N. Do
  A Theory for Sampling Signals from a Union of Subspaces
  IEEE Transactions on Signal Processing, vol. 56, no. 6, pp. 2334–2345 (2008). [A Theory for Sampling Signals from a Union of SubspacesPublisher] [A Theory for Sampling Signals from a Union of SubspacesPDF]
• Y. M. Lu and M. N. Do
  Sampling Signals from a Union of Subspaces
  IEEE Signal Processing Magazine, Special Issue on Compressive Sampling, vol. 25 (2008). [Sampling Signals from a Union of SubspacesPublisher] [Sampling Signals from a Union of SubspacesPDF]
• Y. M. Lu and M. N. Do
  Sampling signals from a union of shift-invariance subspaces
  SPIE Conference on Wavelets Applications in Signal and Image Processing XII, San Diego (2007). [Sampling signals from a union of shift-invariance subspacesPublisher] [Sampling signals from a union of shift-invariance subspacesPDF]
• Y. M. Lu and M. N. Do
  Multidimensional Directional Filter Banks and Surfacelets
  IEEE Transactions on Image Processing, vol. 16, no. 4, pp. 918–931 (2007). [Multidimensional Directional Filter Banks and SurfaceletsPublisher] [Multidimensional Directional Filter Banks and SurfaceletsPDF]
• M. Yan, et al.
  Automatic detection of pelvic lymph nodes using multiple MR sequences
  SPIE Conference on Medical Imaging, San Diego (2007). [Automatic detection of pelvic lymph nodes using multiple MR sequencesPublisher]
• Y. M. Lu and M. N. Do
  Finding optimal integral sampling lattices for a given frequency support in multidimensions
  IEEE International Conference on Image Processing (ICIP), San Antonio, USA (2007). [Finding optimal integral sampling lattices for a given frequency support in multidimensionsPublisher] [Finding optimal integral sampling lattices for a given frequency support in multidimensionsPDF]
  Student Paper Award, ICIP 2007.
• N. Mueller, Y. Lu, and M. N. Do
  Image interpolation using multiscale geometric representations
  SPIE Electronic Imaging, San Jose, USA (2007). [Image interpolation using multiscale geometric representationsPublisher] [Image interpolation using multiscale geometric representationsPDF]
• Y. Lu and M. N. Do
  Video processing using the 3-dimensional surfacelet transform
  Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA (2006). [Video processing using the 3-dimensional surfacelet transformPublisher] [Video processing using the 3-dimensional surfacelet transformPDF]
• Y. Lu and M. N. Do
  Multidimensional nonsubsampled hourglass filter banks: Geometry of passband support and filter design
  Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA (2006), pp. 406–410. [Multidimensional nonsubsampled hourglass filter banks: Geometry of passband support and filter designPublisher] [Multidimensional nonsubsampled hourglass filter banks: Geometry of passband support and filter designPDF]
• Y. Lu and M. N. Do
  A new contourlet transform with sharp frequency localization
  IEEE International Conference on Image Processing (ICIP), Atlanta, USA (2006), pp. 1629–1632. [A new contourlet transform with sharp frequency localizationPublisher] [A new contourlet transform with sharp frequency localizationPDF]
  Most Innovative Paper Award, ICIP 2006.