Publication
A more updated list of my publications may be found on Google Scholar.
Preprints:
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, submitted, 2024.Bagged deep image prior for recoverying 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, submitted, 2023.
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.
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, 2013.
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
Journals:
Signal-to-noise ratio aware minimaxity and higher order asymptotics
Y. Guo, H. Weng, A. Maleki, Transactions on Information Theory, 2023.Analysis of the sensing spectrum for signal recovery under the generalized linear models
J. Ma, J. Xu, 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.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.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.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, 2020Optimization-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.Low noise sensitivity of $\ell_q$-minimization in oversampled systems,
H. Weng, A. Maleki, Information and Inference, 2018.Overcoming the limitations of phase transition by higher-order analysis of regularization techniques
H. Weng, A. Maleki, L. Zheng, Annals of Statistics, 2018.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.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.Minimum complexity pursuit for universal compressed sensing
S. Jalali, A. Maleki, R. Baraniuk, Transactions on Information Theory, 2014.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.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.Noise sensitivity phase transition in compressed sensing*
D. L. Donoho, A. Maleki, and A. Montanari, IEEE Trans. on Inf. Theory, 2011.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]Reproducible research in computational harmonic analysis
D. L. Donoho, A. Maleki, M. Shahram, V. Stodden, I. Rahman, Comput. Sci. Engr., 2009.
Conferences (selected):
(Full list can be found here)
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.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.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,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.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.Global analysis of expectation maximization for mixtures of two Gaussians
J. Xu, D. Hsu, A. Maleki, Neural information processing and systems (NIPS), 2016 [Oral presentation].Does $\ell_p$-minimization outperform $\ell_1$-minimization?
L. Zheng, A. Maleki, X. Wang, T. Long, SPARS, 2015 [Finalist for best paper award].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.Compressed sensing CFAR radar
L. Anitori, A. Maleki, M. Otten, R. Baraniuk, P. Hoogeboom, Proc. IEEE RADARCON, 2012. [Student paper award]Least favorable compressed sensing problems for first order methods
A. Maleki, R. G. Baraniuk, Proc. IEEE Int. Symp. Inform. Theory (ISIT), 2011.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]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]
Dissertation:
Approximate message passing algorithms for compressed sensing
Stanford University PhD thesis, 2011.
* The authors are alphabetically order