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
2019.
Regularized gradient descent: a non-convex recipe for fast joint blind deconvolution and demixing
Shuyang Ling, Strohmer
The non-convex geometry of low-rank matrix optimizationgraphic
Qiuwei Li, Zhihui Zhu, Gongguo Tang
Phase retrieval via randomized Kaczmarz: theoretical guarantees
Yan Shuo Tan, Vershynin
Non-Gaussian observations in nonlinear compressed sensing via Stein discrepancies
Larry Goldstein, Xiaohan Wei
Quantization for low-rank matrix recoverygraphic
Eric Lybrand, Rayan Saab
Simple, direct and efficient multi-way spectral clusteringgraphic
Anil Damle, Victor Minden, Lexing Ying
Through the haze: a non-convex approach to blind gain calibration for linear random sensing models
Valerio Cambareri, Laurent Jacques
Duality of graphical models and tensor networks
Elina Robeva, Anna Seigal
Ensemble-based estimates of eigenvector error for empirical covariance matrices
Dane Taylor, Juan G Restrepo, François G Meyer
A pseudo knockoff filter for correlated features
Jiajie Chen, Anthony Hou, Thomas Y Hou
An efficient algorithm for compression-based compressed sensing
Sajjad Beygi, Shirin Jalali, Arian Maleki, Urbashi Mitra
Mahalanobis distance informed by clusteringgraphic
Almog Lahav, Ronen Talmon, Yuval Kluger
Exact solutions of infinite dimensional total-variation regularized problems
Axel Flinth, Pierre Weiss
Matrix decompositions using sub-Gaussian random matrices
Yariv Aizenbud, Amir Averbuch
Solving (most) of a set of quadratic equalities: composite optimization for robust phase retrieval
John C Duchi, Feng Ruan
Sparsity/undersampling tradeoffs in anisotropic undersampling, with applications in MR imaging/spectroscopy
Monajemi, Donoho
Near-optimal sample complexity for convex tensor completion
Ghadermarzy, Plan, Yilmaz
Bayesian sparse linear regression with unknown symmetric error
Minwoo Chae, Lizhen Lin, David B Dunson
2019s.
Editorial IMA IAI - Information and Inference special issue on optimal transport in data sciences
Gabriel Peyré, Marco Cuturi
On parameter estimation with the Wasserstein distancegraphic
Espen Bernton, Pierre E Jacob, Mathieu Gerber, Christian P Robert
Estimating matching affinity matrices under low-rank constraints
Arnaud Dupuy, Alfred Galichon, Yifei Sun
Uncoupled isotonic regression via minimum Wasserstein deconvolution
Philippe Rigollet, Jonathan Weed
Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration
Jérémie Bigot, Elsa Cazelles, Nicolas Papadakis
The Gromov–Wasserstein distance between networks and stable network invariants
Samir Chowdhury, Facundo Mémoli
Adaptive optimal transport
Montacer Essid, Debra F Laefer, Esteban G Tabak
A central limit theorem for Lp transportation cost on the real line with application to fairness assessment in machine learning
Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
2020.
Computational complexity versus statistical performance on sparse recovery problems
Vincent Roulet, Nicolas Boumal, Alexandre d’Aspremont
State evolution for approximate message passing with non-separable functions
Raphaël Berthier, Andrea Montanari, Phan-Minh Nguyen
Second-order asymptotically optimal statistical classificationgraphic
Lin Zhou, Vincent Y F Tan, Mehul Motani
Low noise sensitivity analysis of graphic-minimization in oversampled systems
Haolei Weng, Arian Maleki
Generalized notions of sparsity and restricted isometry property. Part I: a unified framework
Marius Junge, Kiryung Lee
Empirical Bayes estimators for high-dimensional sparse vectors
K Pavan Srinath, Ramji Venkataramanan
A characterization of the Non-Degenerate Source Condition in super-resolutiongraphic
Vincent Duval
A prototype knockoff filter for group selection with FDR control
Jiajie Chen, Anthony Hou, Thomas Y Hou
Non-convex low-rank matrix recovery with arbitrary outliers via median-truncated gradient descent
Yuanxin Li, Yuejie Chi, Huishuai Zhang, Yingbin Liang
Network topology inference using information cascades with limited statistical knowledge
Feng Ji, Wenchang Tang, Wee Peng Tay, Edwin K P Chong
Robust 1-bit compressed sensing via hinge loss minimization
Martin Genzel, Alexander Stollenwerk
Near-optimal recovery of linear and N-convex functions on unions of convex sets
Anatoli Juditsky, Arkadi Nemirovski
Analysis of hard-thresholding for distributed compressed sensing with one-bit measurements
Johannes Maly, Lars Palzer
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, Ohad Shamir
Phase transitions of spectral initialization for high-dimensional non-convex estimation
Yue M Lu, Gen Li
Quantized compressive sensing with RIP matrices: the benefit of dithering
Chunlei Xu, Laurent Jacques
Maximum number of modes of Gaussian mixtures
Carlos Améndola, Alexander Engström, Christian Haase
One-bit compressed sensing with partial Gaussian circulant matrices
Sjoerd Dirksen, Hans Christian Jung, Holger Rauhut
On the S-instability and degeneracy of discrete deep learning models
Andee Kaplan, Daniel J Nordman, Stephen B Vardeman
Quantifying the estimation error of principal component vectors
Raphael Hauser, Jüri Lember, Heinrich Matzinger, Raul Kangro
Two-sample statistics based on anisotropic kernelsgraphic
Xiuyuan Cheng, Alexander Cloninger, Ronald R Coifman
Phase harmonic correlations and convolutional neural networks
Stéphane Mallat, Sixin Zhang, Gaspar Rochette
Matchability of heterogeneous networks pairsgraphic
Vince Lyzinski, Daniel L Sussman
Analysis of fast structured dictionary learninggraphic
Saiprasad Ravishankar, Anna Ma, Deanna Needell
Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types
Jay Mardia, Jiantao Jiao, Ervin Tánczos, Robert D Nowak, Tsachy Weissman
Stochastic modified equations for the asynchronous stochastic gradient descent
Jing An, Jianfeng Lu, Lexing Ying
CHIRRUP: a practical algorithm for unsourced multiple access
Robert Calderbank, Andrew Thompson
Between hard and soft thresholding: optimal iterative thresholding algorithms
Haoyang Liu, Rina Foygel Barber
Minimal Lipschitz and ∞-harmonic extensions of vector-valued functions on finite graphsgraphic
Miroslav Bačák, Johannes Hertrich, Sebastian Neumayer, Gabriele Steidl
Total variation multiscale estimators for linear inverse problems
Miguel del Álamo, Axel Munk
Erratum to: Robust 1-bit compressed sensing via hinge loss minimization
Martin Genzel, Alexander Stollenwerk