A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…
FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.
Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.
Bayesian framework proves thresholds for multi-graph alignment feasibility.
Proposes a method to choose thresholds for LLM evaluation metrics.
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
Study fairness in ordinal regression using threshold models.
The paper sets thresholds for testing correlation in hypergraphs, distinguishing between independent and correlated states.
The paper considers an investment timing problem appearing in real options theory. Present values from an investment project are modeled by general diffusion process. We prove necessary and sufficient conditions under which an optimal investment time is induced by threshold strategy. We study also the conditions of opt…
The article examines different thresholding methods for improving PAM algorithm in cancer classification.
Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it i…
Detects crypto pump-and-dump schemes with a thresholding-based model.
Comparison of decision curve analysis and cost curves for model evaluation.
New algorithm detects communities even with corrupted data, reaching Kesten-Stigum threshold.
We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of…
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
Sharp thresholds and contiguity for community detection in contextual SBM.
In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to…
Developed a new thresholding method that connects soft and hard thresholding.
Paper finds exact recovery threshold in general hypergraph model.
I show the equivalence between a model of financial contagion and the threshold model of global cascades proposed by Watts (2002). The model financial network comprises banks that hold risky external assets as well as interbank assets. It is shown that a simple threshold model can replicate the size and the frequency o…
Typically, operational risk losses are reported above a threshold. Fitting data reported above a constant threshold is a well known and studied problem. However, in practice, the losses are scaled for business and other factors before the fitting and thus the threshold is varying across the scaled data sample. A report…
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
A simple model explains phase transition in large language models.
Ridge regression is revisited with debiasing and thresholding, offering advantages over Lasso.
This study optimizes multi-modal learning thresholds and algorithms in high dimensions.
This work interprets GELU and related activations via a first-order loss function.
This paper considers the problem of estimating multiple related Gaussian graphical models from a -dimensional dataset consisting of different classes. Our work is based upon the formulation of this problem as group graphical lasso. This paper proposes a novel hybrid covariance thresholding algorithm that can effecti…
Azure (the cloud service provided by Microsoft) is composed of physical computing units which are called nodes. These nodes are controlled by a software component called Fabric Controller (FC), which can consider the nodes to be in one of many different states such as Ready, Unhealthy, Booting, etc. Some of these state…
Graphical lasso may fail to fit models when data points are insufficient.
We study confidence intervals based on hard-thresholding, soft-thresholding, and adaptive soft-thresholding in a linear regression model where the number of regressors may depend on and diverge with sample size . In addition to the case of known error variance, we define and study versions of the estimators when…
We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity (IA). The nonparametric threshold estimator of the integrated variance IV proposed in Mancini 2009 is constructed using observations on a discrete time grid, and precisely it sums up t…
Evaluation of the marginal likelihood plays an important role in model selection problems. The widely applicable Bayesian information criterion (WBIC) and singular Bayesian information criterion (sBIC) give approximations to the log marginal likelihood, which can be applied to both regular and singular models. When the…
New findings on community recovery in SBM with many communities.
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 stochastic block model is one of the oldest and most ubiquitous models for studying clustering and community detection. In an exciting sequence of developments, motivated by deep but non-rigorous ideas from statistical physics, Decelle et al. conjectured a sharp threshold for when community detection is possible in…
The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard dictionaries often lead to non-optimal performance when used for computing thresholde…
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…
New findings support a new community recovery threshold for Stochastic Block Model with many communities.
New algorithm achieves strong consistency in binary non-uniform hypergraph classification.
In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using -reg…
Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires expensive equipment and the extraction of blood samples, which are inconvenient f…
A new SSL method uses instance-dependent thresholds to improve accuracy.
In financial markets, low prices are generally associated with high volatilities and vice-versa, this well known stylized fact usually being referred to as leverage effect. We propose a local volatility model, given by a stochastic differential equation with piecewise constant coefficients, which accounts of leverage a…
Finite-time queue peaks in stochastic networks have logarithmic scaling after geometric thresholds.
Study stability thresholds of big line bundles, proving bounds and generalizing results.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
A simple thresholding technique improves graph selection in neural connectivity studies.