Bayesian model predicts sexual function recovery after prostatectomy.
problem Predicting sexual function recovery after prostatectomy surgery.
method Bayesian model using pre-treatment information.
result Personalized predictions of sexual function recovery.
We construct a complete bounded immersed null holomorphic curve in C^3, which is a recovery of the previous version of the paper Calc. Var. and PDE's vol 36 (2009); Erratum: to appear in Calc. Var. and PDE's, doi:10.1007/s00526-009-0226-5 by the last three authors on this subject.
Paper characterizes phase transition of TV minimization for sparse-gradient signal recovery.
problem Characterizing phase transitions of TV minimization for sparse-gradient signal recovery.
method Combines AMP conjectured phase transition curve and high-dimensional convex geometry.
result Fully characterizes the phase transition curve of TV minimization.
Combines BTEM and T-PLS for accurate spectral recovery and calibration.
problem Calibrating pure spectra of minority components in mixtures without prior knowledge.
method Band target entropy minimization (BTEM) and target partial least squares (T-PLS).
result Estimated amounts from BTEM-T-PLS similar to MCR-ALS on simple mixtures, superior on complex ones.
Nuclear norm minimization (NNM) has recently gained significant attention for its use in rank minimization problems. Similar to compressed sensing, using null space characterizations, recovery thresholds for NNM have been studied in \cite{arxiv,Recht_Xu_Hassibi}. However simulations show that the thresholds are far fro…
Method recovers obstacles from travel times on curved surfaces.
problem Recovering obstacles from travel times on curved surfaces.
method Extending Noakes and Stoyanov's method to Riemannian surfaces with curvature constraints.
result Obstacles can be recovered from travel times on Riemannian surfaces under certain curvature conditions.
New method constructs solution operators for PDEs with prescribed support properties.
problem Constructing solution operators for under/overdetermined PDEs with specific support properties.
method Using a recovery on curves condition and taking smooth averages over curves, we obtain integral solution operators and representation formulas.
result Our method leads to integral representation formulas for overdetermined PDEs and solution operators for underdetermined PDEs.
Group Shapley evaluates feature groups in business data, improving explainability in AI.
problem Evaluating the importance of feature groups in business and economic data.
method Developed Group Shapley and a significance testing procedure based on chi-square approximation.
result Market-related variables are identified as the most influential feature group.
Dual labor market model explains low inflation despite low unemployment.
problem Low inflation despite low unemployment during economic recovery.
method Minimal model of dual labor market to explore factors affecting Phillips curve.
result Changes in bargaining power and labor supply elasticity make Phillips curve flat.
DAC provides interpretable feature importance curves for tree ensembles.
problem Challenges in understanding feature importance and prediction in tree ensembles.
method Disentangled Attribution Curves (DAC) method to visualize feature importance.
result DAC improves interpretability of tree ensembles while maintaining accuracy.
We derive an arbitrage free relationship between recovery swap rates, digital default swap spreads and conventional CDS spreads, and argue that the fair forward recovery rate used in recovery swaps must contain a convexity premium over the expected recovery value.
Method uses Seq2Seq learning to automatically generate recovery commands for ICT systems.
problem Manual decision-making for recovery commands is time-consuming and error-prone.
method Seq2Seq neural network model trained on past logs and commands.
result The model can estimate accurate recovery commands from new failures.
A new model explains U- and Swoosh-shaped stock price recovery during the COVID-19.
problem Modeling stock price recovery during the COVID-19 with V- and L-shaped recovery.
method Introducing a sentiment variable θ to quantify investor sentiment and simulate U- and Swoosh-shaped recovery. result The model explains U- and Swoosh-shaped recovery of sectoral indices with positive sentiment.
This paper improves support recovery in universal one-bit compressed sensing.
problem Support recovery in one-bit compressed sensing for sparse signals.
method Proposes approximate support recovery and superset recovery algorithms with polynomial-time complexity.
result Achieves improved support recovery with fewer measurements compared to existing methods.
This work provides a guaranteed tensor recovery method by combining low-rankness and smoothness priors.
problem Guaranteed tensor recovery with theoretical guarantees for low-rank and smoothness priors.
method Developed a new regularization term that combines low-rankness and smoothness priors, proving exact recovery guarantees.
result Rigorously proved exact recovery guarantees for tensor completion and tensor robust principal component analysis.
This paper tackles tensor recovery from noisy and multi-level quantized measurements.
problem Tensors from multi-level quantized measurements.
method Nonconvex optimization problem with alternating proximal gradient descent.
result The recovery error diminishes to zero with increasing tensor dimensions.
We discuss a general notion of "sparsity structure" and associated recoveries of a sparse signal from its linear image of reduced dimension possibly corrupted with noise. Our approach allows for unified treatment of (a) the "usual sparsity" and "usual ℓ1 recovery," (b) block-sparsity with possibly overlapping blo…
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
IRKSN algorithm achieves sparse recovery with wider applicability conditions.
problem Sparse recovery challenges due to NP-hard nature and restrictive conditions.
method IRKSN algorithm based on k-support norm regularizer. result Achieves sparse recovery with explicit constants and standard linear rate.
A framework for discrete structure recovery using iterative algorithms.
problem Recovering various discrete structures from data.
method General iterative algorithm for discrete structure recovery.
result Linear convergence of the proposed algorithm under certain conditions.
In recent years research on credit risk modelling has mainly focused on default probabilities. Recovery rates are usually modelled independently, quite often they are even assumed constant. Then, however, the structural connection between recovery rates and default probabilities is lost and the tails of the loss distri…
Study finds the cutoff for exact recovery in Gaussian mixture models.
problem Determining the separation of cluster centers for exact recovery in Gaussian mixture models.
method Used information theory and SDP relaxation of K-means clustering. result Sharp threshold for exact recovery of cluster labels without assuming cluster center symmetry.
Paper introduces robust subspace recovery methods for noisy data.
problem Finding a low-dimensional subspace in corrupted data.
method Explains advantages and disadvantages of subspace recovery algorithms.
result Outlines unsolved problems in robust subspace recovery.
Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.
problem Optimizing portfolios with a new risk measure under known or uncertain distributions.
method Existence results for mean-risk optimal portfolios under different distributional assumptions.
result Portfolio selection under Recovery Average Value at Risk provides better control over liabilities.
Paper proves conditions for nonconvex matrix recovery to avoid spurious local minima.
problem Ensuring no spurious local minima in nonconvex matrix recovery.
method Sharp restricted isometry bounds proof technique.
result RIP constant of δ < 1/2 is necessary and sufficient for exact recovery.
Study finds economic cycle affects bank loan recovery rates.
problem Understanding how economic conditions impact bank loan recovery rates.
method Used a two-state Markov switching mechanism to model credit cycle states and analyze recovery rates.
result Recovery rates and their determinants differ between good and bad economic times.
The paper improves conditions for unique recovery in homomorphic sensing of subspaces.
problem Unique recovery of points in a linear subspace from their images under linear maps.
method Tighter and simpler conditions for unique recovery in single and subspace arrangement cases, extending to noise stability.
result Conditions for unique recovery in homomorphic sensing are improved and unified.
HSNLD solves robust Hankel recovery efficiently and robustly.
problem Robust Hankel recovery of sparse outliers and missing entries.
method Hankel Structured Newton-Like Descent (HSNLD) algorithm.
result HSNLD achieves linear convergence independent of the condition number.
Guarantees recovery of compressible signals from adversarial noise.
problem Recovering compressible signals from noise and adversarial attacks.
method Extends adversarial defense framework to ℓ0, ℓ2, and ℓ∞ norms. result Recovery guarantees for various signal recovery methods under different noise types.
New method improves dictionary recovery from over-realized models.
problem Theoretical guarantees for model recovery in dictionary learning are limited.
method Search over larger over-realized models to facilitate dictionary recovery.
result Model recovery can be upper-bounded by empirical risk and generalization gap.
Unified framework for pattern recovery in penalized and thresholded estimation.
problem Pattern recovery in penalized and thresholded estimation methods.
method Defining a novel pattern notion based on subdifferentials, introducing accessibility and noiseless recovery conditions.
result Unified and extended conditions for pattern recovery in a broad class of penalized estimators.
Paper tackles robust sparse recovery in impulsive noise, using CMN and ADMM.
problem Sparse signal recovery in the presence of heavy-tailed impulsive noise.
method Exploits Continuous Mixed Norm (CMN) and Alternating Direction Method of Multipliers (ADMM).
result CMN leads to near optimal recovery in blind conditions.
Paper tackles graph estimation with approximate recovery criteria, matching exact recovery bounds in many cases.
problem Estimating the graph of an Ising model with approximate recovery criteria.
method Adopting approximate recovery criterion, using Fano's inequality and graph ensembles to derive lower bounds.
result Lower bounds on sample complexity match exact recovery bounds in many cases, indicating similar difficulty.
Paper explores exact recovery of communities in weighted graphs using Gaussian and exponential distributions.
problem Exact recovery of communities in weighted graphs with Gaussian and exponential distributions.
method Introduces a new semi-metric to describe conditions for exact recovery and analyzes conditions for both complete and incomplete graphs.
result Necessary and sufficient conditions for exact recovery are asymptotically tight and applicable to both complete and incomplete graphs.
New sampling strategies for graph signal recovery show faster convergence rates.
problem Signal recovery on irregular graphs.
method Two sampling strategies: random and experimentally designed. Proposed recovery strategies based on these.
result Experimentally designed sampling converges faster than random sampling for irregular graphs.
The study examines how modernizing settlement infrastructure affects inside money elasticity and network efficiency.
problem Understanding the impact of modernizing settlement infrastructure on inside money elasticity and network efficiency.
method Constructed a panel dataset of 809 reform events across 24 advanced economies, decomposed into economic channels and phases, and used a T2S event-study and synthetic control method.
result Modernizing settlement infrastructure generates network-conditional balance sheet efficiencies, with an estimated +13.4 percent efficiency recovery from 2027-2032.
Guarantees sparse recovery for neural networks with iterative hard thresholding.
problem Recovering sparse network weights in neural networks.
method Structural properties of sparse network weights and iterative hard thresholding algorithm.
result Simple iterative hard thresholding algorithm recovers sparse network weights exactly using linear memory.
This study models credit default swap premiums with a stochastic recovery rate.
problem Analyzing credit default swap premiums with varying recovery rates.
method Develops a model using stochastic recovery rates.
result Models credit default swap premiums effectively with a stochastic recovery rate.
BCD-Net uses identical CNN structures for image recovery in undersampled imaging.
problem Challenges in obtaining accurate images from undersampled or noisy measurements.
method Incorporates image mapping CNN into BCD signal recovery method using alternating direction method of multipliers.
result Significantly more accurate image recovery compared to existing methods.
New conic relaxations improve sparse signal recovery with fewer observations.
problem Recovering sparse signals from noisy data.
method Comparing two semidefinite relaxations for sparse linear regression.
result Dong's relaxation requires fewer observations for exact recovery.
We propose and analyze a generic method for community recovery in stochastic block models and degree corrected block models. This approach can exactly recover the hidden communities with high probability when the expected node degrees are of order logn or higher. Starting from a roughly correct community partition …
A new method for gradient recovery on manifold data without tangent spaces.
problem Gradient recovery schemes for data on discretized manifolds.
method Parametric Polynomial Preserving Recovery (PPPR) on manifolds.
result Superconvergence and high curvature stability of PPPR.
Paper refines null space conditions for nuclear norm minimization in low-rank matrix recovery.
problem Establishing conditions for successful nuclear norm minimization recovery of low-rank matrices.
method Developed new null space conditions for nuclear norm minimization, proving their necessity and sufficiency.
result Weak null space condition is sufficient but not necessary for nuclear norm minimization recovery, providing a new necessary and sufficient condition.
Paper studies superconvergence on surface meshes using gradient recovery.
problem Proving superconvergence on deviated surfaces.
method Introduces geometric supercloseness and an algorithmic framework for gradient recovery.
result Validates theoretical results with numerical examples.
New risk measure improves creditor protection in financial regulation.
problem Current solvency requirements fail to control the size of recovery on creditors' claims.
method Developed Recovery Value at Risk (Recovery VaR) to control recovery on creditors' claims.
result Recovery VaR flexibly controls recovery on creditors' claims and integrates protection needs into management incentives.
New method improves traffic data recovery for streaming data.
problem Improve data quality in traffic data for ITS.
method Online robust tensor recovery algorithm leveraging spatio-temporal correlations and local consistency.
result Significantly improved computational efficiency and high recovery accuracy.
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-ℓ1 regularization. While the current theory provides promis…
Study on recovering hidden communities from data matrices.
problem Recovering a hidden community of cardinality K from a symmetric data matrix.
method Analyzes two types of asymptotic recovery guarantees for weak and exact recovery, deriving conditions for both sufficient and necessary recovery.
result Derives sufficient and necessary conditions for recovery under mild assumptions on probability distributions P and Q.