arXiv research

Symmetry helps VI recover certain statistics.

This paper advances FL algorithms for composite optimization and statistical recovery.

Paper explores limits of high-order clustering with planted structures.

Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.

A new method enhances signal recovery with FDR control.

New method explains computational barriers in high-dimensional statistical models.

Paper reconciles minimax rates and optimal recovery rates for noisy observations.

Paper tackles sparse recovery with shuffled labels, establishing statistical and computational limits.

Study shows generative priors improve rank-one matrix recovery with optimal sample complexity.

A new model for dynamic covariance recovery in neuroimaging data.

These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models. The first two lectures cover the principles of signal recovery from linear measurements in terms of minimax risk. Subsequent lectures demonstrate the application of these principles to several pract…

New SAE algorithm proves feature recovery for LLMs with theoretical guarantees.

The study analyzes aftershocks of stock market crashes using statistical methods.

New insights into variable selection with different model assumptions.

In the past decade, sparse and low-rank recovery have drawn much attention in many areas such as signal/image processing, statistics, bioinformatics and machine learning. To achieve sparsity and/or low-rankness inducing, the $\ell_1$ norm and nuclear norm are of the most popular regularization penalties due to their co…

New findings on computational limits for estimating hidden structures.

Improved algorithm for partial recovery of tree-structured graphs with noisy data.

In this note we compare two recently proposed semidefinite relaxations for the sparse linear regression problem by Pilanci, Wainwright and El Ghaoui (Sparse learning via boolean relaxations, 2015) and Dong, Chen and Linderoth (Relaxation vs. Regularization A conic optimization perspective of statistical variable select…

We introduce a general framework to handle structured models (sparse and block-sparse with possibly overlapping blocks). We discuss new methods for their recovery from incomplete observation, corrupted with deterministic and stochastic noise, using block-$\ell_1$ regularization. While the current theory provides promis…

Flat minima lead to better generalization in low-rank matrix recovery models.

QAOA matches classical tensor power iteration in spiked tensor model recovery.

Paper quantizes heavy-tailed data for near optimal estimation rates.

Survey on factor models and their applications in econometrics.

Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.

New method robust to semi-random sparse recovery, nearly-linear time.

New model for community detection with side information improves recovery accuracy.

The paper tracks patient recovery using graphs of joint movement data.

This work improves distribution recovery from sparse data using Random Forest implicit regularization.

WPCA improves subspace recovery robustness to outliers.

The paper tackles partial inference in structured prediction using a convex optimization approach.

Develops a robust GMM estimator for outlier-tolerant inference.

The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex analysis and optimization over spaces of tensors is now gaining much interest due to its potential applications to signal processing, statistics…

Low-degree method fails to predict robust subspace recovery problem.

Optimal recovery framework for non-IID data in Hilbert spaces.

New framework uses score-based priors to solve ill-conditioned polynomial equations, improving signal recovery from noisy data.

We consider the problem of estimating the support of a vector $β^* \in \mathbb{R}^{p}$ based on observations contaminated by noise. A significant body of work has studied behavior of $\ell_1$-relaxations when applied to measurement matrices drawn from standard dense ensembles (e.g., Gaussian, Bernoulli). In this paper,…

In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize bo…

Develops PRPCA for smooth image recovery combining low-rank and smoothness.

Exact recovery method for community detection in Gaussian mixtures with dependent noise.

Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.

New tensor recovery method uses Riemannian optimization on Segre manifold.

The stochastic block model (SBM) is a random graph model with different group of vertices connecting differently. It is widely employed as a canonical model to study clustering and community detection, and provides a fertile ground to study the information-theoretic and computational tradeoffs that arise in combinatori…

Active seriation recovers item order from noisy pairwise similarity measurements.

Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture mu…

In this paper we study the problem of exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions of the players alone. We consider sparse linear influence games --- a parametric class of graphical games with linear payoffs, and represented by directed gra…

New method recovers graph latent positions under edge differential privacy.

We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently r…

Paper analyzes IHT's performance in sparse recovery problems.