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本帖最后由 colimae 于 2021-5-11 20:01 编辑
InfInf(2012-2020):
2012.
Dear Information and Inference Reader
Calderbank, Donoho, Shawe-Taylor, Tanner
he masked sample covariance estimator: an analysis using matrix concentration inequalities
Richard Y. Chen, Alex Gittens, Joel A. Tropp
Eigenvector synchronization, graph rigidity and the molecule problem
Mihai Cucuringu, Amit Singer, David Cowburn
Semi-supervised single- and multi-domain regression with multi-domain training
Michaeli, Eldar, Sapiro
2013.
Approximation of points on low-dimensional manifolds via random linear projections
Mark A. Iwen, Mauro Maggioni
Compressive principal component pursuit
John Wright, Arvind Ganesh, Kerui Min, Yi Ma
Tangent space estimation for smooth embeddings of Riemannian manifolds
Hemant Tyagi, Elif Vural, Pascal Frossard
State evolution for general approximate message passing algorithms, with applications to spatial coupling
Javanmard, Montanari
Exact and stable recovery of rotations for robust synchronization
Lanhui Wang, Amit Singer
2014.
Cramér–Rao bounds for synchronization of rotations
Nicolas Boumal, Amit Singer, P.-A. Absil, Vincent D. Blondel
Sigma–Delta quantization of sub-Gaussian frame expansions and its application to compressed sensing
Felix Krahmer, Rayan Saab, Özgür Yilmaz
Higher order Sobol' indices........................Art B. Owen, Josef Dick, Su Chen
Phase retrieval from power spectra of masked signals
Afonso S. Bandeira, Yutong Chen, Dustin G. Mixon
Finite sample posterior concentration in high-dimensional regression
Nate Strawn, Artin Armagan, Rayan Saab, Lawrence Carin, David Dunson
Non-asymptotic analysis of tangent space perturbation
Daniel N. Kaslovsky, François G. Meyer
1-Bit matrix completion
Davenport, Plan, van den Berg, Wootters
Living on the edge: phase transitions in convex programs with random data
Dennis Amelunxen, Martin Lotz, Michael B. McCoy, Joel A. Tropp
Scaling law for recovering the sparsest element in a subspace
Laurent Demanet, Paul Hand
Persistent homology transform for modeling shapes and surfaces
Katharine Turner, Sayan Mukherjee, Doug M. Boyer
Deterministic Bayesian information fusion and the analysis of its performance
Gaurav Thakur
2015.
Graph connection Laplacian and random matrices with random blocks
El Karoui, Hau-tieng Wu
Disparity and optical flow partitioning using extended Potts priors
Xiaohao Cai, Jan Henrik Fitschen, Mila Nikolova, Gabriele Steidl, Martin Storath
On spectral properties for graph matching and graph isomorphism problems
Fiori, Sapiro
Compressed subspace matching on the continuum
Mantzel, Romberg
Riemannian metrics for neural networks I: feedforward networks
Riemannian metrics for neural networks II: recurrent networks and learning symbolic data sequences
Yann Ollivier
Tensor sparsification via a bound on the spectral norm of random tensors
Nam H. Nguyen, Petros Drineas, Trac D. Tran
Model selection with low complexity priors
Samuel Vaiter, Mohammad Golbabaee, Jalal Fadili, Gabriel Peyré
CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion
Jeffrey D. Blanchard, Jared Tanner, Ke Wei
Guarantees of total variation minimization for signal recovery
Jian-Feng Cai, Weiyu Xu
Replication procedure for grouped Sobol' indices estimation in dependent uncertainty spaces
Laurent Gilquin, Clémentine Prieur, Elise Arnaud
ERRATUM--------Finite sample posterior concentration in high-dimensional regression
N. Strawn, A. Armagan, R. Saab, L. Carin, D. Dunson
2016.
Robust subspace recovery by Tyler's M-estimator
Teng Zhang
Super-resolution radar.......................Heckel, Morgenshtern, Soltanolkotabi
A null-space-based weighted l1 minimization approach to compressed sensing
Shenglong Zhou, Naihua Xiu, Yingnan Wang, Lingchen Kong, Hou-Duo Qi
Super-resolution of point sources via convex programming
Carlos Fernandez-Granda
Detecting the large entries of a sparse covariance matrix in sub-quadratic time
Ofer Shwartz, Boaz Nadler
Near-optimal estimation of simultaneously sparse and low-rank matrices from nested linear measurements
Bahmani, Romberg
Total variation regularization on Riemannian manifolds by iteratively reweighted minimization
Philipp Grohs, Markus Sprecher
On the optimality of averaging in distributed statistical learning
Jonathan D. Rosenblatt, Boaz Nadler
Stable low-rank matrix recovery via null space properties
Maryia Kabanava, Richard Kueng, Holger Rauhut, Ulrich Terstiege
2016s.
Special issue: Deep learning....................Francis Bach, Tomaso Poggio
Deep Haar scattering networks
Xiuyuan Cheng, Xu Chen, Stéphane Mallat
On invariance and selectivity in representation learning
Fabio Anselmi, Lorenzo Rosasco, Tomaso Poggio
A theoretical framework for deep transfer learning
Tomer Galanti, Lior Wolf, Tamir Hazan
GSNs: generative stochastic networks
Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski,
Éric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent
2017.
2018.
Superresolution without separation
Geoffrey Schiebinger, Elina Robeva, Benjamin Recht
Quantized minimax estimation over Sobolev ellipsoids
Yuancheng Zhu, John Lafferty
One-bit compressive sensing of dictionary-sparse signals
Baraniuk, Foucart, Needell, Plan, Wootters
Demixing sines and spikes: Robust spectral super-resolution in the presence of outliers
Carlos Fernandez-Granda, Gongguo Tang, Xiaodong Wang, Le Zheng
When is non-trivial estimation possible for graphons and stochastic block models?‡
Audra McMillan, Adam Smith
Algorithms for learning sparse additive models with interactions in high dimensions*
Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause
Weighted mining of massive collections of P-values by convex optimization
Edgar Dobriban
Fast, robust and non-convex subspace recoverygraphic
Gilad Lerman, Tyler Maunu
Universality laws for randomized dimension reduction, with applications
Samet Oymak, Joel A Tropp
Sketching for large-scale learning of mixture models
Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez
Gaussian approximation of general non-parametric posterior distributions
Zuofeng Shang, Guang Cheng
Iterative reconstruction of rank-one matrices in noise
Alyson K Fletcher, Sundeep Rangan
A convex program for mixed linear regression with a recovery guarantee for well-separated datagraphic
Paul Hand, Babhru Joshi
MC2: a two-phase algorithm for leveraged matrix completion
Armin Eftekhari, Michael B Wakin, Rachel A Ward
New approach to Bayesian high-dimensional linear regression
Shirin Jalali, Arian Maleki
Structured sampling and fast reconstruction of smooth graph signals
Gilles Puy, Patrick Pérez
A spectral assignment approach for the graph isomorphism problemgraphic
Stefan Klus, Tuhin Sahai
Isometric sketching of any set via the Restricted Isometry Property
Oymak, Recht, Soltanolkotabi
Conditional expectation estimation through attributable components
Esteban G Tabak, Giulio Trigila
Gradient descent with non-convex constraints: local concavity determines convergencegraphic
Rina Foygel Barber, Wooseok Ha
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