arXiv research

Modeling firm default with a variable threshold based on management decisions.

Sharp threshold for simple connectivity in random 2-complexes.

Investigates probability of error in structured thresholding bandit problems.

Adaptive sampling framework for varying probabilities.

Maximizes precision and recall at top by thresholding posterior probability.

Study bounds deepfake detection error probability using robust statistics.

A new method combines bagging and threshold-moving for imbalanced data.

Sharp bounds on crash probability and loss from option quotes.

Develops RES metrics for stable rare-event forecasting evaluation.

Framework for consistent binary classification with complex metrics.

New method calculates sensitivity of system failure probability.

We investigate the probability distributions of the recurrence intervals $τ$ between consecutive 1-min returns above a positive threshold $q>0$ or below a negative threshold $q<0$ of two indices and 20 individual stocks in China's stock market. The distributions of recurrence intervals for positive and negative thresho…

We define the information threshold in Bayesian decision-making.

Square percolation determines threshold for group divergence in random graphs.

Bayesian framework proves thresholds for multi-graph alignment feasibility.

Threshold found for hyperbolicity in random Coxeter groups.

New method estimates probability of simulator output exceeding threshold.

The binary symmetric stochastic block model deals with a random graph of $n$ vertices partitioned into two equal-sized clusters, such that each pair of vertices is connected independently with probability $p$ within clusters and $q$ across clusters. In the asymptotic regime of $p=a \log n/n$ and $q=b \log n/n$ for fixe…

The interplay between computational efficiency and statistical accuracy in high-dimensional inference has drawn increasing attention in the literature. In this paper, we study computational and statistical boundaries for submatrix localization. Given one observation of (one or multiple non-overlapping) signal submatrix…

Optimal rates for learning hidden tree structures are determined.

The paper develops methods to predict the probability of achieving a user goal in a task, ensuring the system alerts when the probability falls below a threshold.

Majority bit estimation in noisy random recursive DAGs.

The recurrence interval of extreme returns can be predicted with high accuracy.

Spectral method detects communities in sparse hypergraphs, achieving detection threshold.

New algorithm achieves strong consistency in binary non-uniform hypergraph classification.

A new machine learning method calculates failure probability efficiently and accurately.

To estimate a sparse linear model from data with Gaussian noise, consilience from lasso and compressed sensing literatures is that thresholding estimators like lasso and the Dantzig selector have the ability in some situations to identify with high probability part of the significant covariates asymptotically, and are …

The paper provides high-probability bounds on false discovery proportions in conformal inference.

We study the pricing of credit derivatives with asymmetric information. The managers have complete information on the value process of the firm and on the default threshold, while the investors on the market have only partial observations, especially about the default threshold. Different information structures are dis…

In this paper, we study the sensitivity of the spectral clustering based community detection algorithm subject to a Erdos-Renyi type random noise model. We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general…

Polynomial-time algorithm solves random parity games with high probability.

Unified framework for pattern recovery in penalized and thresholded estimation.

We study confidence intervals based on hard-thresholding, soft-thresholding, and adaptive soft-thresholding in a linear regression model where the number of regressors $k$ may depend on and diverge with sample size $n$. In addition to the case of known error variance, we define and study versions of the estimators when…

Faster method detects bias in lending, hiring, and policing decisions.

Fewer degrees of freedom can train deep networks, showing a sharp phase transition.

CTI produces efficient prediction intervals with guaranteed coverage.

Optimizes waiting thresholds for node health states in Azure.

Machine learning predicts movie genres from summaries with high accuracy.

We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting. The GRASS estimate of the conditional dependence graph is obtained by thresholding the elements of the sample covar…

Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.

Study proposes active learning method for estimating robust regions in uncertain function evaluations.

A new method for choosing thresholds in data sequences without assuming distribution.

Study compares two methods for predicting extreme atmospheric events.

Analyzes biased random walks and corrupted intervals in adversarial settings.

This paper sets thresholds for recovering vertex correspondences in partially correlated graphs.

Paper finds exact recovery threshold in general hypergraph model.

Optimal purchasing policy for mean-reverting items with a finite deadline.

Study on homology of random Čech complexes on manifolds with boundary.