Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
problem Removing noise from deep neural network weights for better approximation.
method Model weights as signal + noise, use random matrix theory to estimate thresholds, evaluate using cosine similarity.
result Proposed threshold estimation method improves approximation quality.
Developed a new thresholding method that connects soft and hard thresholding.
problem Connecting soft and hard thresholding methods in data analysis.
method Scaled soft thresholding method with empirical scaling values.
result Found two sources of over-fitting in the scaled soft thresholding method.
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…
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…
A new algorithm improves sample complexity for thresholding in Monte Carlo Tree Search.
problem Determining if the root node value of a tree is at least a given threshold.
method Developed a δ-correct sequential sampling algorithm based on the Track-and-Stop strategy.
result Ratio-based modification of D-Tracking strategy reduces sample complexity and computational cost.
We discuss the turnpike property for optimal investment and consumption problems. We find there exists a threshold value that determines the turnpike property for investment policy. The threshold value only depends on the Sharpe ratio, the riskless interest rate and the discount rate. We show that if utilities behave a…
The paper finds optimal threshold strategies for insurance companies with a positive terminal value at creeping ruin.
problem Optimizing dividend payments in an insurance company's surplus process with a positive terminal value at creeping ruin.
method Using fluctuation theory, the paper derives explicit formulas for the objective function and shows the optimality of threshold strategies.
result Threshold strategies are optimal for the dividend optimization problem under certain conditions.
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…
We win EVA2025 by estimating extreme precipitation events using Peaks Over Thresholds and martingale testing.
problem Estimating the probability of extreme precipitation events with limited data.
method Modeling Peaks Over Thresholds with an exponential distribution and using martingale testing for evaluation.
result Our method outperforms other approaches in estimating extreme precipitation events.
Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of …
The paper discusses thresholds and bounds for accuracy in binary classification systems.
problem The accuracy of binary classification systems and its dependence on prevalence.
method Analyzing the precision-prevalence curve and negative predictive value-prevalence curve to find thresholds and bounds.
result Thresholds (φe and φn) bound various accuracy metrics (Fβ, F1, FM, MCC) and the ratio of maximum accuracy to prevalence. Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…
MTSSL optimizes threshold τ for better semi-supervised learning performance.
problem Optimizing the threshold τ for effective semi-supervised learning.
method Meta-Thresholding approach to optimize τ during training.
result Optimal values of τ are not necessary for achieving similar performance.
Recently, a novel family of biologically plausible online algorithms for reducing the dimensionality of streaming data has been derived from the similarity matching principle. In these algorithms, the number of output dimensions can be determined adaptively by thresholding the singular values of the input data matrix. …
In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named Sparse Tensor Alternating Thresholding for Singular Value Decomposition (STAT-SVD) is proposed. The proposed procedure featu…
Study analyzes FIT schemes under market and regulatory uncertainty.
problem Tackles uncertainty in feed-in tariffs and their impact on investment thresholds.
method Uses semi-analytical real options framework to model and compare FIT schemes.
result Increasing regulatory uncertainty lowers investment thresholds for FIT schemes.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
problem Identifying outliers in a set of rewards where the threshold is a function of all rewards.
method Adaptively updated confidence interval for the threshold based on previous rounds' estimates, balancing exploration of individual arms and the outlier threshold.
result Efficient algorithm with reduced sample complexity for outlier detection.
Method selects the best deep learner for time-series prediction using Bayesian networks.
problem Selecting the most effective deep learning model for time-series prediction.
method Bayesian network selects deep learners based on input variables and cluster training data.
result Threshold value determines which deep learners predict time-series data robustly.
Optimal thresholds ensure curves remain embedded in flows.
problem Preserving the embeddedness of elastic flows of curves.
method Variational characterization and minimization of bending energy.
result Optimal thresholds for preserving embeddedness are found.
Improved estimation of hedge fund tail risks using a novel model.
problem Estimation inefficiencies and need for manual threshold selection in extreme value regression models.
method Extended tail regression model with automatic threshold selection and artificial censoring.
result Significant link between tail risks and factors like equity momentum and financial stability index.
We consider the problem of the optimal trading strategy in the presence of linear costs, and with a strict cap on the allowed position in the market. Using Bellman's backward recursion method, we show that the optimal strategy is to switch between the maximum allowed long position and the maximum allowed short position…
Inference over tails is usually performed by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. However, the choice of such threshold is critical and can affect the inferential results. Extreme value mixture models have been defined to estimate the threshold using the full dat…
Optimal rank-adaptive matrix estimation from linear measurements.
problem Estimating high-dimensional matrices from linear measurements with adaptive rank selection.
method Combines Least-Squares estimator with universal singular value thresholding.
result Algorithm performance nearly matches fundamental limits.
Improved AI lung ultrasound segmentation using expert confidence values.
problem Label uncertainty in lung ultrasound due to subjective interpretation by radiologists.
method Designing a data annotation protocol capturing expert confidence, training AI on binarized labels with confidence thresholds.
result Improved AI segmentation and better clinical outcomes (e.g., S/F oxygenation ratio estimation, patient readmission prediction).
Comparison of decision curve analysis and cost curves for model evaluation.
problem Evaluating classification performance across different operating contexts.
method Comparison of Decision Curve Analysis (DCA) and Cost Curves.
result DCA and Cost Curves are closely related, with Brier curves being more generally applicable.
Sparse reconstruction approaches using the re-weighted l1-penalty have been shown, both empirically and theoretically, to provide a significant improvement in recovering sparse signals in comparison to the l1-relaxation. However, numerical optimization of such penalties involves solving problems with l1-norms in the ob…
Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) scores as anomalies. This presents a practical limitation on the applicabili…
New algorithms estimate function levels with near-optimal efficiency.
problem Estimating points where an unknown function exceeds a given threshold.
method Relates to adaptive experimental design methods for linear bandits in RKHS.
result Proves nearly optimal sample complexity bounds for level set estimation.
We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence. We do not ass…
In this paper, we present theorems specifying the critical values for series associated with debts arranged in the order of their duration.
Study optimal stopping times under regime-switching models with constraints.
problem Optimal stopping times for discounted payoffs on a regime-switching geometric Brownian motion.
method Solve variational inequality to find value functions and optimal thresholds.
result Existence and expressions of optimal stopping times under specific conditions.
Improves interpretability of anomaly scores in GBRBM-based detection.
problem Difficulty in setting a proper threshold for anomaly scores.
method Proposes a measure based on cumulative distribution and uses simulated annealing for evaluation.
result Established a guideline for setting the threshold using the interpretable measure.
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…
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
problem Forecasting financial tail risks using realized volatility and nonlinear thresholds.
method Bayesian Markov Chain Monte Carlo method for model estimation; nonlinear threshold regression specification.
result The proposed framework produces competitive tail risk forecasts compared to GARCH and Realized-GARCH models.
The paper provides high-probability bounds on false discovery proportions in conformal inference.
problem Existing methods fail to provide high-probability bounds on the realized false discovery proportion.
method Constructing a high-probability envelope for the empirical distribution function of null conformal p-values by sampling from their joint distribution.
result Establishes finite-sample, distribution-free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds.
DeepTopPush improves accuracy at the top for complex classification tasks.
problem Minimizing irrelevant samples above a threshold in binary classification.
method Proposes a new method for end-to-end training of deep networks to minimize loss at the top.
result Demonstrates excellent performance on visual recognition and real-world applications.
New method corrects bias in CVaR estimation for extreme risks.
problem Limited data above VaR leads to poor CVaR estimation.
method Bias-corrected peaks-over-threshold (POT) estimation using GPD.
result Asymptotically unbiased CVaR estimator with lower threshold.
We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter ρ. Suppose the co- variance graph formed by thresholding the entries of the sample covariance matrix at ρ is decomposed into connected components. We show that the vertex-partition induced by the thresh…
The paper examines smoothness of value function in consumption-investment models with borrowing constraints.
problem Investor's optimal consumption and investment under consumption-wealth utility and borrowing constraint.
method Second-order smoothness of value function, optimal consumption-investment policy in feedback form, smooth fit condition.
result The value function is second-order smooth and the constraint is binding under certain conditions.
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 study the relaxation dynamics of a financial market just after the occurrence of a crash by investigating the number of times the absolute value of an index return is exceeding a given threshold value. We show that the empirical observation of a power law evolution of the number of events exceeding the selected thre…
Paper studies community detection in censored hypergraphs using information theory.
problem Community detection in censored hypergraphs with missing values.
method Information-theoretic approach, polynomial-time algorithm, spectral algorithm with refinement.
result Derives information-theoretic threshold for exact recovery of community structure.
A fast method estimates Gaussian mixture components without iterative fitting.
problem Estimating the number of components in high-dimensional Gaussian mixtures.
method Center data, compute singular values, and count above a threshold.
result The estimator consistently recovers the true number of components under mild separation condition.
Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.
problem Forecasting high NO2 concentration episodes for effective air quality management.
method Compared 10 probabilistic forecasting models for NO2 concentration prediction.
result Quantile gradient boosted trees model outperformed others in predicting NO2 concentration distributions.
SyncRank recovers global ranking from noisy comparisons with theoretical guarantees.
problem Recovering a global ranking from noisy pairwise comparisons.
method Complex-valued data model and SDP relaxation for exact ranking recovery.
result SyncRank achieves exact ranking recovery with high probability above a critical noise threshold of O(sqrt(n / log n)).
Develops a statistical test for IV, improving feature selection reliability.
problem Lack of statistical justification in conventional IV-based feature selection.
method Establishes connection with Jeffreys divergence and proposes a nonparametric hypothesis test.
result The J-Divergence test provides rigorous guarantees and is more reliable than traditional IV thresholds.
Unified formula for training dynamics of linear networks combining lazy and balanced regimes.
problem Training dynamics of linear networks in two distinct setups: lazy and balanced/active.
method Unified formula for the evolution of the learned matrix, combining lazy and balanced regimes.
result Unified formula allows for rapid convergence and low rank bias, proving a complete phase diagram.
The paper studies phase transitions in random matrices and tensor unfolding for detecting signals.
problem Phase transitions in singular values and vectors of large random matrices.
method Analysis of singular values and vectors of long rectangular random matrices, and tensor unfolding algorithm for asymmetric rank-one spiked tensor models.
result An exact threshold for tensor unfolding to detect signals, independent of unfolding procedure.