Publication

A more updated list of my publications may be found on Google Scholar.

• 2025:

  • Looking ahead with UniLASSO: New avenues for understanding and development
    A. Maleki, H. Weng, Harvard Data Science Review, 2025. 

  • Newfluence: Boosting model interpretability and understanding in high dimensions
    H. Zou, A. Auddy, Y. Kwon, K. Rahnamarad, A. Maleki, to be presented at ICML 2025.

  • Multilook coherent imaging: theoretical guarantees and algorithms
    X. Chen, S. Jana, C. Metzler, S. Jalali, A. Maleki, submitted 2025. 

  • Certified machine unlearning under high-dimensional settings
    H. Zou, A. Auddy, Y. Kwon, K. Rahnamarad, A. Maleki, submitted 2025.

  • Signal-to-noise ratio aware minimax analysis of sparse linear regression sed
    S. Ghosh, Y. Guo, H. Weng, A. Maleki, submitted 2025. 

  • Comprehensive examination of unrolled networks for solving linear inverse problems
    E. Chen, X. Chen, A. Maleki, S. Jalali, Entropy, 2025. 

  • Is speckle noise more challenging to mitigate than additive noise?
    R. Malekian, H Xing, A. Maleki, Transactions on Information Theory, 2025. 

  • Phase transitions in phase only compressed sensing
    J. Chen, L. Lai, A. Maleki, International Symposium on Information Theory (ISIT), 2025. 

  • Theoretical analysis of leave-one-out cross validation for non-differentiable penalties under the high-diemensional asymptotics
    H. Zou, A. Auddy, K. Rad, A. Maleki, AISTATs, 2025.

  • Efficient Multilook Coherent Imaging with temporally dependant speckle noise
    X. Chen, C. Metzler, A. Maleki, S. Jalali, Unconventional Imaging, Sensing, and Adaptive Optics, 2025. 

  • Deep memory unrolled network for solving linear inverse problems,
    E. Chen, X. Chen, S. Jalali, A. Maleki, Sampling Theory and Applications (Oral presentation).  

  • Monte Carlo-based efficient image reconstruction in coherent imaging with speckle noise
    X. Chen, C. Metzler, A. Maleki, S. Jalali, International Symposium on Biomedical Imaging (ISBI), (Oral presentation).  

• 2024:

  • A note on minimax risk of sparse linear regression
    Y. Guo, S. Ghosh, H. Weng, A. Maleki, submitted, 2024.

  • Approximate leave-one-out cross validation for regression with $\ell_1$-regulaizer
    A. Auddy, H. Zou, K. Rad, A. Maleki, Transactions on Information Theory, 2024.

  • Bagged deep image prior for recovering images in the presence of speckle noise
    X. Chen, Z. Hou,  A. Maleki, C. Metzler, S. Jalali, ICML, 2024.

  • Approximate leave-one-out cross validation for regression with $\ell_1$-regulaizer
    A. Auddy, H. Zou, K. Rad, A. Maleki, Artificial Intelligence and Statistics (AISTATs), 2024  [Oral presentation]. 

  • New Approach to Coherent Imaging in the presence of speckle noise
    X. Chen, C. Metzler, A. Maleki, S. Jalali, Unconventional Imaging, Sensing, and Adaptive Optics, 2024.  

• 2023

  • Towards designing optimal ensing matrix for signal recovery for the generalized linear models
    J. Ma, J. Xu, A. Maleki, Transactions on Information Theory, 2023. 

  • Signal-to-noise ratio aware minimaxity and higher order asymptotics
    Y. Guo, H. Weng, A. Maleki, Transactions on Information Theory, 2023.

  • Sharp concentration results for heavy-tailed distributions
    M. Bakhshizadeh, A. Maleki, V. de La Pena, Information and Inference, 2023.

  • Multilook compressive sensing in the presence of speckle noise, 
    X. Chen, Z. Hou, A. Maleki, C. Metzler, S. Jalali, Neural information processing and systems (NeurIPS), Inverse problems workshop, 2023. 

• 2022

  • Compressed sensing in the presence of speckle noise
    W. Zhou, S. Jalali, A. Maleki,  Transactions on Information Theory, 2022.

  • Does SLOPE outperform bridge regression?
    S. Wang, H. Weng, A. Maleki, Information and Inference, 2022.

• 2021

  • Consistent risk estimation in high-dimensional linear regression
    J. Xu, A. Maleki, K. Rahnamarad, Transactions on Information Theory, 2021.

  • Using black-box compression algorithms for phase retrieval
    M. Bakhshizadeh, A. Maleki, S. Jalali, Transactions on Information Theory, 2021.

  • Information-theoretic limits for phase retrieval with subsampled Haar sensing matrices
    R. Dudeja, J. Ma, A. Maleki, Transactions on Information Theory, 2021.

  • Spectral methods for phase retrieval: an expectation propagation perspective
    J. Ma, R. Dudeja, J. Xu, A. Maleki, X. Wang, Transactions on Information Theory, 2021.

  • Analysis of the sensing spectrum for signal recovery under the generalized linear models
    J. Ma, J. Xu, A. Maleki, Neural information processing and systems (NeurIPS), 2021, 

  • Mismatched data detection in Massive MIMO
    C. Jeon, A. Maleki, C. Studer, Trans. Signal Processing, 2021.

  • Optimal data detection and signal estimation in systems with input noise
    Ramina Ghods, C Jeon, A. Maleki, C. Studer, Trans. Signal Processing, 2021

• 2020

  • A scalable estimate of extra-sample prediction error via approximate leave-one-out
    K. Rahnamarad, A. Maleki, Journal of Royal Statistical Society (JRSS-B), 2020.

  • Rigorous analysis of spectral methods for random orthogonal estimators
    R. Dudeja, M. Bakhshizadeh, J. Ma, A. Maleki, Transactions on Information Theory, 2020.

  • Which bridge estimator is optimal for variable selection?
    S. Wang, H. Weng, A. Maleki, Annals of Statistics, 2020.

  • On the Gaussianity of the Kolmogorov complexity of mixing sequences
    M. Austern, A. Maleki, Transactions on Information Theory,  2020

  • Approximate Leave-One-Out for High-dimensional Non-differentiable Learning Problems  
    S. Wang, W. Zhou, A. Maleki, H. Lu, V. Mirrokni, under minor revision in Journal of Machine Learning Research (JMLR), 2020.

  • Error bounds for estimating out-of-sample prediction error using leave-one-out cross-validation in high-dimensions
    K. Rahnama-rad, W. Zhou, A. Maleki, Artificial Intelligence and Statistics (AISTAT), 2020.
    
    

• 2019

  • Optimization-based AMP for Phase Retrieval: The impact of initialization and $\ell_2$-regularization
    J. Ma, J. Xu, A. Maleki, Trasactions on Information Theory, 2019.             

• An efficient compression-based compressed sensing algorithm
  S. Beigi, S. Jalali, A. Maleki, U. Mitra, Information and Inference, 2019.    

• Minimax linear estimation of retargetted means
  D. Hirshberg, A. Maleki, J. Zubizarreta, under minor revision at JMLR, 2019.

•  Low noise sensitivity of $\ell_q$-minimization in oversampled systems,
  H. Weng, A. Maleki, Information and Inference, 2019.
  
  

• 2018

  • Overcoming the limitations of phase transition by higher-order analysis of regularization techniques
    H. Weng, A. Maleki, L. Zheng, Annals of Statistics, 2018.

  • Benefits of over-parametrization with EM
    J. Xu, D. Hsu, A. Maleki, Neural information processing and systems (NeurIPS), 2018.

  • Approximate leave-one-out for fast parameter tuning in high-dimensions
    S. Wang, W. Zhou, H. Lu, A. Maleki, V. Mirrokni, International conference on machine learning (ICML), 2018.

  • Approximate message passing for amplitude-based optimization
    J. Ma, J. Xu, A. Maleki, International conference on machine learning (ICML), 2018.

  • Compressive phase retrieval for structured signals
    M. Bakhshizadeh, A. Maleki, S. Jalali, Proc. IEEE Int. Symp. Inform. Theory (ISIT), 2018.
    
    

• 2017

  • New approach to Bayesian high-dimensional linear regression
    S. Jalali, A. Maleki, Information and Inference, 2017.

  •  Does $\ell_p$-minimization outperform $\ell_1$-minimization?
    L. Zheng, A. Maleki, X. Wang, T. Long, Transactions on InformationTheory, 2017.

  • Consistent parameter estimation for LASSO and approximate message passing
    A. Mousavi, A. Maleki, R. Baraniuk, Annals of Statistics, 2017.

  • $\ell_p$-based CAMP with application to sparse stepped frequency radar
    L. Zheng, Q. Liu, X. Wang, A. Maleki, Signal Processing, 2017.

  • Optimally-tuned nonparametric linear equalization for massive MU-MIMO systems
    R. Ghods, C. Jeon,  G. Mirza, A. Maleki, C. Studer, Asilomar conference on signals, systems, and computers, 2017. 

  •  Moving beyond sparsity by transferring expert knowledge
    A. Maleki,  Conference on Information Sciences and Systems, invited paper, 2017.

  •  VLSI design of nonparametric equalizer for massiv MU-MIMO
    C. Jeon,  G. Mirza, R. Ghods, A. Maleki, C. Studer,  Asilomar conference on signals, systems, and computers, 2017.
    
    

• 2016

  • From denoising to compressed sensing
    C. Metzler, A. Maleki, R. Baraniuk, Transactions on Information Theory, 2016.

  •  From compression to compressed sensing
    S. Jalali, A. Maleki, Appl. Comput. Harmon. Anal., 2016. 

  • Global analysis of expectation maximization for mixtures of two Gaussians
    J. Xu, D. Hsu, A. Maleki, Neural information processing and systems (NeurIPS), 2016 [Oral presentation].

  • On the performance of mismatched data detection in large MIMO systems
    Charles Jeon, A. Maleki, C. Studer, International Symposium on Information Theory (2016).
    
    

• 2015

  •  Does $\ell_p$-minimization outperform $\ell_1$-minimization?
    L. Zheng, A. Maleki, X. Wang, T. Long, SPARS, 2015 [Finalist for best paper award].

  •  BM3D-AMP:A new image recovery algorithm based on BM3D denoising
    C. Metzler, A. Maleki, R. Baraniuk, International Conference on Image Processing, 2012.

  •   Optimality of large MIMO detection via approximate message passing
    C. Jeon, R. Ghods, A. Maleki, C. Studer, International Conference on Information Theory, 2015.

  • Optimal large MIMO data detection with transmit impairments
    R. Ghods, C. Jeon, A. Maleki, C. Studer,  Allerton Conference on Communication, Control and Computing, 2015.
    
    

• 2014

  • Minimum complexity pursuit for universal compressed sensing
    S. Jalali, A. Maleki, R. Baraniuk, Transactions on Information Theory, 2014.

  • Compressed sensing over $\ell_p$ balls: Minimax mean square error*  
    D. L. Donoho, I. M. Johnstone, A. Maleki, A. Montanari, under major revision in Annals of Statistics, 2014.

• 2013

  • Design and analysis of compressed sensing radar detectors
    L. Anitori, A. Maleki, M. Otten, R. Baraniuk, P.  Hoogeboom, Trans. Sig. Processing, 2013.

  • Anisotropic nonlocal means
    A. Maleki, M. Narayan, R. G. Baraniuk, Appl. Comput. Harmon. Anal., 2013.

  • Asymptotic analysis of complex LASSO via complex approximate message passing (CAMP)
    A. Maleki, L. Anitori, Z. Yang, R. G. Baraniuk, Transactions Information Theory, 2013. 

  • Maximin analysis of message passing for recovering group sparse signals
    A. Taeb, A. Maleki, C. Studer, R. Baraniuk, SPARS 2013.

  • Iterative Thresholding algorithms for sparse inverse covariance estimation
    D. Guillot, B. Rajaratnam, B. Rolfs, A. Maleki, I. Wong,  Neural information processing and systems (NIPS), 2013.

• 2012

  • VLSI design of approximate message passing for signal restoration and compressive sensing
    P. Maechler, C. Studer, D. Bellasi, A. Maleki, A. Burg, N. Felber, H. Kaeslin, R. Baraniuk, IEEE J. Emerg. Sel. Topics on Circuits Systems, 2012.

  • Suboptimality of nonlocal means for images with sharp edges
    A. Maleki, M. Narayan, R. G. Baraniuk, Appl. Comput. Harmo. Anal., 2012.

  • Rate distortion analysis of the directional wavelet
    A. Maleki, B. Rajaei, H. Pourreza, IEEE Trans. Image Processing, 2012. 

  • Compressed sensing CFAR radar
    L. Anitori, A. Maleki, M. Otten, R. Baraniuk, P. Hoogeboom, Proc. IEEE  RADARCON, 2012. [Student paper award]

  • Minimum complexity pursuit: Stability Analysis
    S. Jalali, A. Maleki, International Symposium on Information Theory, 2012.

• 2011

  • Noise sensitivity phase transition in compressed sensing*
    D. L. Donoho, A. Maleki, and A. Montanari, IEEE Trans. on  Inf. Theory, 2011.

  • Parameterless, optimal approximate message passing
    A. Mousavi, A. Maleki, R. Baraniuk, draft.     

  • Constructing message passing algorithms for compressed sensing,
          D. L. Donoho, A. Maleki, A. Montanari, draft

  • Least favorable compressed sensing problems for first order methods
    A. Maleki, R. G. Baraniuk, Proc. IEEE Int. Symp. Inform. Theory (ISIT), 2011.

• Selected publications prior to 2010

  • Message passing algorithms for compressed sensing*
    D. L. Donoho, A. Maleki, and A. Montanari, Proc. of Natl. Acad.  of Sci. (PNAS), 2009. [full version]

  •  Optimally tuned iterative thresholding algorithm for compressed sensing
    A. Maleki, D. L. Donoho, IEEE J. Sel. Topics Signal Process., April 2010. [arxiv]

  •  Analysis of approximate message passing algorithm
    A. Maleki, A. Montanari, Proc. IEEE Conf. Inform. Science and Systems (CISS), 2010. [invited]

  • Message passing algorithms for compressed sensing, part 1: motivation and construction*
    D. L. Donoho, A. Maleki, and  A. Montanari, Proc. Inform. Theory Work. (ITW), 2010. [invited]

  • Message passing algorithms for compressed sensing, part 2: analysis and validation*
    D. L. Donoho, A. Maleki, and A. Montanari, Proc. Inform. Theory Work. (ITW), 2010. [invited]

  • Coherence analysis of iterative thresholding algorithms
    A. Maleki, Proc. Allerton Conf. Communication, Control, and Computing, 2009. [arxiv]

  • Phase transition of iterative thresholding algorithms
    A. Maleki and D. L. Donoho, Proc. Work. Struc. Parc. Rep. Adap. Signaux (SPARS), 2009.  [Finalist for best paper award]

  • Reproducible research in computational harmonic analysis
    D. L. Donoho, A. Maleki, M. Shahram, V. Stodden, I. Rahman, Comput. Sci. Engr.,  2009.  

  • A solution to the drawbacks of spectral clustering
    N. Asgharbeigi and A. Maleki, Proc. IEEE Int. Conf. Pattern Recognition (ICPR), 2008.

  • Coherent and Heterogeneous approach for clustering
    A. Maleki, N. Asgharbeigi, Proc. of IEEE artificial intelligence and pattern recognition (AIPR), 2008. [More details here]