Spectral Clustering of graphs with the Bethe Hessian. Fast Randomized Semi-Supervised Clustering. How does dblp detect coauthor communities. In, El Alaoui, A. and Krzakala, F. (2018). High-dimensional analysis of semidefinite relaxations for sparse principal components. Florent Krzakala Ecole Normale Supérieure Paris, Sorbonne Université, LightOn Verified email at ens.fr Paolo Tombesi Università di Camerino Verified email at unicam.it Hugo Defienne Lecturer and Marie Curie-Sklodowska fellow, University of Glasgow Verified email at glasgow.ac.uk For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). In, Barbier, J., Dia, M., Macris, N., Krzakala, F., Lesieur, T. and Zdeborová, L. (2016). They arise in signal processing, statistical inference, machine learning, communication theory, and other fields. The Mutual Information in Random Linear Estimation. On the distribution of the largest eigenvalue in principal components analysis. Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning. Exact asymptotics for phase retrieval and compressed sensing with random generative priors. Scaling Up Echo-State Networks With Multiple Light Scattering. (2017). In, El Alaoui, A., Krzakala, F. and Jordan, M. (2020). Who is Afraid of Big Bad Minima? Who is Afraid of Big Bad Minima? Optical Reservoir Computing using multiple light scattering for chaotic systems prediction. Estimation in the spiked Wigner model: A short proof of the replica formula. Spectral density of the non-backtracking operator. 2020 A Landscape Analysis of Constraint Satisfaction Problems. Swept Approximate Message Passing for Sparse Estimation. Florent Krzakala Ecole Normale Superieure ... Google H-index: 46: Number of Google Citations: 7,834: Number of Articles on DBLP: 151: External Links. Bai, Z. and Yao, J. Statistical and computational phase transitions in spiked tensor estimation. (2008). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Approximate Message Passing with Restricted Boltzmann Machine Priors. 3729th, G2R Switzerland Ranking Lelarge, M. and Miolane, L. (2019). Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model. Non-adaptive pooling strategies for detection of rare faulty items. Phase transitions and sample complexity in Bayes-optimal matrix factorization. The largest eigenvalues of finite rank deformation of large Wigner matrices: Convergence and nonuniversality of the fluctuations. Inferring Sparsity: Compressed Sensing using Generalized Restricted Boltzmann Machines. Clustering from Sparse Pairwise Measurements. When the spike comes from a prior that is i.i.d. So please proceed with care and consider checking the Unpaywall privacy policy. Bai, Z. and Yao, J. At the same time, Twitter will persistently store several cookies with your web browser. Passed & Spurious: analysing descent algorithms and local minima in spiked matrix-tensor model. the dblp computer science bibliography is funded by: Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation. Johnstone, I. M. (2001). Light-in-the-loop: using a photonics co-processor for scalable training of neural networks. G2R World Ranking Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices. (2018). Ann. (2008). Fluctuations of the free energy of the spherical Sherrington–Kirkpatrick model. Kernel computations from large-scale random features obtained by Optical Processing Units. ; El Alaoui, A. and Krzakala, F. (2018). Julien Launay, Iacopo Poli, Kilian Müller, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan: Light-in-the-loop: using a photonics co-processor for scalable training of neural networks. (2012). Amini, A. Deformed ensembles of random matrices. Their combined citations are counted only for the first article. Generalisation error in learning with random features and the hidden manifold model. Adaptive damping and mean removal for the generalized approximate message passing algorithm. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. To protect your privacy, all features that rely on external API calls from your browser are turned off by default. Banerjee, D. and Ma, Z. full text of this article because we are not able to identify you as Spectral redemption: clustering sparse networks. Supplement to “Fundamental limits of detection in the spiked Wigner model”. Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models. Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. last updated on 2020-10-27 22:14 CET by the dblp team, all metadata released as open data under CC0 1.0 license, see also: Terms of Use | Privacy Policy | Imprint. Compressed sensing of approximately-sparse signals: Phase transitions and optimal reconstruction. Statistical physics-based reconstruction in compressed sensing. 103rd. (2009). Approximate Survey Propagation for Statistical Inference. Multi-layer generalized linear estimation. Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units. What is the meaning of the colors in the coauthor index? Capitaine, M., Donati-Martin, C. and Féral, D. (2009). We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. Approximate Message-Passing Decoder and Capacity Achieving Sparse Superposition Codes. Therefore, both estimation and detection undergo the same transition in this random matrix model. Finite sample approximation results for principal component analysis: A matrix perturbation approach. Blind calibration for compressed sensing: State evolution and an online algorithm. Random Projections through multiple optical scattering: Approximating kernels at the speed of light. Phase Diagram and Approximate Message Passing for Blind Calibration and Dictionary Learning. https://projecteuclid.org/euclid.aos/1590480037, © So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar. Variational Free Energies for Compressed Sensing. (2018). Modelling the influence of data structure on learning in neural networks. MMSE of probabilistic low-rank matrix estimation: Universality with respect to the output channel. Testing in high-dimensional spiked models. While we did signal Twitter to not track our users by setting the "dnt" flag, we do not have any control over how Twitter uses your data. TRAMP: Compositional Inference with TRee Approximate Message Passing. The mutual information in random linear estimation. Florent Krzakala École polytechnique fédérale de Lausanne Verified email at epfl.ch Luca Leuzzi, PhD Institute of Nanotechnology, CNR, Italy Verified email at cnr.it Guilhem Semerjian LPT … All settings here will be stored as cookies with your web browser. Phase transitions in sparse PCA. Paul, D. (2007). On consistency and sparsity for principal components analysis in high dimensions. So please proceed with care and consider checking the Twitter privacy policy. Constrained Low-rank Matrix Estimation: Phase Transitions, Approximate Message Passing and Applications. SourceAnn. Guerra, F. (2001). Blind Calibration in Compressed Sensing using Message Passing Algorithms. Analysis of gradient-flow in spiked matrix-tensor models. Mutual information in rank-one matrix estimation. Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation. Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization. 48 (2020), no. a subscriber. Central limit theorems for eigenvalues in a spiked population model. Add a list of references from , , and to record detail pages. Spectral detection in the censored block model. Principled Training of Neural Networks with Direct Feedback Alignment. arXiv preprint, Banks, J., Moore, C., Vershynin, R., Verzelen, N. and Xu, J. Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model. Benaych-Georges, F. and Nadakuditi, R. R. (2011). 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Profile was last updated at November 12, 2020, 12:09 am, EPFL : École polytechnique fédérale de Lausanne, Machine Learning & Artificial Intelligence, Computational Linguistics & Speech Processing, Ranking for Top Computer Science Universities 2020, Ranking for Top Scientists in Computer Science and Electronics 2020, 6th Edition, Ranking for Top Scientists in Computer Science and Electronics 2019, 5th Edition, Ranking for Top Scientists in Computer Science and Electronics 2018, Special Issues for Journals With Impact Factor, 2017/2017, Conference Ranking : Top Computer Science Conferences, 2017/2017, Impact Factor for Top Journals of Computer Science and Electronics, 2017, Impact Factor for Top Journals of Computer Science and Electronics, 2016, Impact Factor for Top Journals of Computer Science and Electronics, 2015, How to chart a successful research career by Prof Alan Johnson, Top H-Index for Scholars of Computer Science & Electronics, 2014. Optimality and sub-optimality of PCA I: Spiked random matrix models. Phase Transitions and Sample Complexity in Bayes-Optimal Matrix Factorization. Our proofs are based on Gaussian interpolation methods and a rigorous incarnation of the cavity method, as devised by Guerra and Talagrand in their study of the Sherrington–Kirkpatrick spin-glass model. The following articles are merged in Scholar. Detection limits in the high-dimensional spiked rectangular model. Scampi: a robust approximate message-passing framework for compressive imaging. Belief Propagation Reconstruction for Discrete Tomography. CoRR abs/2006.01475 (2020) Péché, S. (2006). Banerjee, D. (2018). Random projections through multiple optical scattering: Approximating Kernels at the speed of light. Franz, S. and Parisi, G. (1998). The hard-core model on random graphs revisited. Comparative Study for Inference of Hidden Classes in Stochastic Block Models. Intensity-only optical compressive imaging using a multiply scattering material : a double phase retrieval system. Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. You need to opt-in for them to become active. DatesReceived: October 2017Revised: February 2019First available in Project Euclid: 26 May 2020, Permanent link to this documenthttps://projecteuclid.org/euclid.aos/1590480037, Digital Object Identifierdoi:10.1214/19-AOS1826, Mathematical Reviews number (MathSciNet) MR4102679, Subjects Primary: 62H25: Factor analysis and principal components; correspondence analysis Secondary: 62H15: Hypothesis testing 60G15: Gaussian processes 60F05: Central limit and other weak theorems, KeywordsHypothesis testing random matrix models contiguity spin–glasses Sherrington–Kirkpatrick model replica–symmetry, El Alaoui, Ahmed; Krzakala, Florent; Jordan, Michael. Robust error correction for real-valued signals via message-passing decoding and spatial coupling. Recipes for metastable states in spin glasses. Multi-Layer Generalized Linear Estimation. Guide2Research uses the information to contact you about our relevant content. If you have a personal subscription to this journal, A. and Wainwright, M. J. Scaling up Echo-State Networks with multiple light scattering. Fluctuations of the free energy of the spherical Sherrington–Kirkpatrick model with ferromagnetic interaction. Mutual Information in Rank-One Matrix Estimation. Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures. Gibbs States and the Set of Solutions of Random Constraint Satisfaction Problems. Asymptotic power of sphericity tests for high-dimensional data. Variational free energies for compressed sensing. Robust Phase Retrieval with the Swept Approximate Message Passing (prSAMP) Algorithm. On the Universality of Noiseless Linear Estimation with Respect to the Measurement Matrix. Gibbs states and the set of solutions of random constraint satisfaction problems. (2016). The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices. Phase Transitions and Computational Difficulty in Random Constraint Satisfaction Problems. Add a list of citing articles from and to record detail pages. Their combined citations are counted only for the first article. The largest eigenvalue of rank one deformation of large Wigner matrices. Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula. Baik, J. and Lee, J. O. Generalisation dynamics of online learning in over-parameterised neural networks. Epidemic mitigation by statistical inference from contact tracing data. Eigenvalues of large sample covariance matrices of spiked population models. Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup. Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (or : How to Prove Kabashima's Replica Formula). load references from crossref.org and opencitations.net. Florent Krzakala École polytechnique fédérale de Lausanne Email verificata su epfl.ch Pan Zhang Institute of Theoretical Physics, Chinese Academy of Sciences Email verificata su itp.ac.cn Andrea Montanari Professor of Electrical Engineering and Statistics, Stanford University Email … Reservoir Computing meets Recurrent Kernels and Structured Transforms. Phase diagram and approximate message passing for blind calibration and dictionary learning. So please proceed with care and consider checking the Internet Archive privacy policy. Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula. Perry, A., Wein, A. S., Bandeira, A. S. and Moitra, A. Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices. High-temperature Expansions and Message Passing Algorithms. Approximate message-passing decoder and capacity-achieving sparse superposition codes. (2018). Decoding from Pooled Data: Sharp Information-Theoretic Bounds. Asymptotic mutual information for the binary stochastic block model. All Conferences. Google Scholar Profile; List of Publications on DBLP ... 11:28 pm Guide2Research Ranking is based on Google Scholar H-Index. Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models. Sharp detection in PCA under correlations: All eigenvalues matter. Asymptotic normality and analysis of variance of log-likelihood ratios in spiked random matrix models. Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime. Statist., Volume 48, Number 2 (2020), 863-885. Phase Transitions in the Coloring of Random Graphs. This establishes the maximal region of contiguity between the planted and null models. This supplement (El Alaoui, Krzakala and Jordan (2020)) contains the proof of convergence of the moments of the overlap $R_{1,*}$ thereby completing the proof of Theorem 10, and the proof of Lemma 14. Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy. (2009). Onatski, A., Moreira, M. J. and Hallin, M. (2014). Féral, D. and Péché, S. (2007). First-order transitions and the performance of quantum algorithms in random optimization problems. across coordinates, we prove that the log-likelihood ratio of the spiked model against the nonspiked one is asymptotically normal below a certain reconstruction threshold which is not necessarily of a “spectral” nature, and that it is degenerate above. Nadler, B. Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Phase Transitions, Optimal Errors and Optimality of Message-Passing in Generalized Linear Models. Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Further information on the performance of the optimal test is also provided. On convergence of approximate message passing. arXiv preprint, Krzakala, F., Xu, J. and Zdeborová, L. (2016). Effective potential in glassy systems: Theory and simulations. If you are already logged in, then you may need The Gaussian equivalence of generative models for learning with two-layer neural networks. Model Selection for Degree-corrected Block Models. Péché, S. (2014). Compressed sensing under matrix uncertainty: Optimum thresholds and robust approximate message passing. Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction. Sum rules for the free energy in the mean field spin glass model. In. The following articles are merged in Scholar. Information-theoretic thresholds from the cavity method. Spectral detection on sparse hypergraphs. Asymptotics of sample eigenstructure for a large dimensional spiked covariance model. Compressed sensing and Approximate Message Passing with spatially-coupled Fourier and Hadamard matrices. For more information, check out our privacy policy. Decoding From Pooled Data: Phase Transitions of Message Passing. Guerra, F. (2003). On sample eigenvalues in a generalized spiked population model. Sparse Estimation with the Swept Approximated Message-Passing Algorithm. Approximate message-passing for convex optimization with non-separable penalties. Onatski, A., Moreira, M. J. and Hallin, M. (2013). 62H25: Factor analysis and principal components; correspondence analysis, 60F05: Central limit and other weak theorems, Optimality and sub-optimality of PCA I: Spiked random matrix models, Central limit theorems for cavity and local fields of the Sherrington-Kirkpatrick model, Spin glass models from the point of view of spin distributions, The Aizenman-Sims-Starr and Guerras schemes for the SK model with multidimensional spins, Variational representations for the Parisi functional and the two-dimensional Guerra–Talagrand bound, Disorder chaos in the Sherrington–Kirkpatrick model with external field, Computational and statistical boundaries for submatrix localization in a large noisy matrix. to update your profile to register your subscription. Aizenman, M., Lebowitz, J. L. and Ruelle, D. (1987). Add open access links from to the list of external document links (if available). In Proceedings of the 31st Conference on Learning Theory (COLT) 75 410–438. Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. El Alaoui, A. and Jordan, M. I. Some rigorous results on the Sherrington–Kirkpatrick spin glass model. Supplemental files are immediately available to subscribers. Fast Phase Retrieval for High Dimensions: A Block-Based Approach. Fundamental limits of symmetric low-rank matrix estimation. Fast phase retrieval for high dimensions: A block-based approach. Adaptive Damping and Mean Removal for the Generalized Approximate Message Passing Algorithm. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar. Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines. It is known that this threshold also marks a phase transition for estimating the spike: the latter task is possible above the threshold and impossible below. Deshpande, Y., Abbé, E. and Montanari, A. Compressed Sensing under Matrix Uncertainty: Optimum Thresholds and Robust Approximate Message Passing. The Mutual Information in Random Linear Estimation Beyond i.i.d. High-dimensional generalized linear models are basic building blocks of current data analysis tools including multilayers neural networks. Spectral Clustering of Graphs with the Bethe Hessian. Spectral Detection in the Censored Block Model. Phase transition in the detection of modules in sparse networks. Performance Limits for Noisy Multi-Measurement Vector Problems. In. Phase transitions and optimal algorithms in high-dimensional Gaussian mixture clustering. Fundamental limits of detection in the spiked Wigner model. Spectral Method for Multiplexed Phase Retrieval and Application in Optical Imaging in Complex Media. Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16). Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Lesieur, T., Krzakala, F. and Zdeborová, L. (2015). Phase recovery from a Bayesian point of view: the variational approach. Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices. then please login. The committee machine: Computational to statistical gaps in learning a two-layers neural network. In. Robust phase retrieval with the swept approximate message passing (prSAMP) algorithm. Streaming Bayesian inference: theoretical limits and mini-batch approximate message-passing. (2016). What is the meaning of the colors in the publication lists? A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines. Broken replica symmetry bounds in the mean field spin glass model. Streaming Bayesian inference: Theoretical limits and mini-batch approximate message-passing. Hiding Quiet Solutions in Random Constraint Satisfaction Problems. Decoding from pooled data: Phase transitions of message passing. Baik, J. and Lee, J. O. Phase recovery from a Bayesian point of view: The variational approach. Phase retrieval in high dimensions: Statistical and computational phase transitions. Marvels and Pitfalls of the Langevin Algorithm in Noisy High-dimensional Inference. Intensity-only optical compressive imaging using a multiply scattering material and a double phase retrieval approach. Florent Krzakala Ecole Normale Supérieure Paris, Sorbonne Université, LightOn Verified email at ens.fr Alaa Saade Research Engineer at Deepmind Verified email at google.com Angélique Drémeau ENSTA Bretagne Verified email at ensta-bretagne.fr Rank-one matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method. (2018). Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Detection limits in the high-dimensional spiked rectangular model.

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