A new method for selecting model order in statistical shape models.
problem Choosing the right model order in PDMs for optimal performance.
method Information-theoretic criteria for selecting model order.
result The new method provides a better trade-off between overfitting and underfitting.
SCAMP clusters data by selecting candidate clusters that follow shape constraints, avoiding the need for tuning parameters.
problem Clustering data in high-dimensional space with unknown number of clusters.
method SCAMP formulates clustering as a search and selection problem, using shape constraints and preference functions to select clusters.
result SCAMP can be run multiple times to assess clustering uncertainty, providing a robust method for data annotation.
Two binary Sine Cosine Algorithms improve feature selection in medical datasets.
problem Optimizing feature selection from medical datasets to enhance model accuracy.
method Proposed SBSCA and VBSCA algorithms using S-shaped and V-shaped transfer functions.
result SBSCA and VBSCA outperform four other binary optimization algorithms in medical datasets.
UniShape improves time series classification by selecting relevant subsequences.
problem Classifying time series data requires capturing interpretable shapelets.
method UniShape uses a shape-aware adapter to aggregate multiscale subsequences into class tokens.
result UniShape achieves state-of-the-art classification performance.
Shape-constrained symbolic regression improves model extrapolation with prior knowledge.
problem Improving model extrapolation with prior knowledge in symbolic regression.
method Shape-constrained symbolic regression using evolutionary algorithms with interval arithmetic.
result Models with shape constraints have improved extrapolation but lower accuracy on test sets.
METASET selects diverse unit cells for efficient data-driven metamaterial design.
problem Imbalanced datasets in unit cells can bias data-driven metamaterial design.
method METASET uses similarity metrics and DPPs to select diverse subsets of unit cells.
result Smaller, diverse subsets improve search process and structural performance.
This paper proposes the adaptation of Support Vector Data Description (SVDD) to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a variant called Slim-MK-SVDD that is able to produce a tighter frontier around the data. For the sake of comparison, the equivalent methods are also developed for O…
ML helps select variables for minimum-variance portfolios, reducing risk and improving performance.
problem Optimizing minimum-variance portfolios with relevant predictors.
method Parameterized minimum-variance portfolio weights using a large pool of firm-level characteristics and their transformations.
result ML-selected predictors lead to lower risk and better performance in minimum-variance portfolios.
A new method for analyzing shapes and forms using additive models on manifolds.
problem Analyzing shapes and forms under geometric transformations.
method Extending generalized additive regression to models for shapes/forms using squared geodesic distance and Riemannian L2-Boosting algorithm. result Automated model selection and intuitive visualization of covariate effects in shape/form space.
This paper introduces a new shape-based image reconstruction technique applicable to a large class of imaging problems formulated in a variational sense. Given a collection of shape priors (a shape dictionary), we define our problem as choosing the right elements and geometrically composing them through basic set opera…
We solve S-shaped utility portfolio selection with SD constraints using algorithms and neural networks.
problem Optimizing portfolios with S-shaped utility functions under SD constraints.
method First-order SD constraint solution, numerical algorithm for SSD, neural network approach.
result Effective numerical and neural network solutions for SSD constrained problems.
The paper explores risk measures and arbitrage in financial markets.
problem Quantifying and managing risk in financial markets.
method Introduces new risk measure axioms and characterizes arbitrage conditions.
result Derives the consistent price interval for financial contracts.
We introduce a method called multi-scale local shape analysis, or MLSA, for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine lea…
Researchers use shape analysis to recover protein structures from Cryo-EM data.
problem Recovering the three-dimensional backbone structure of single polypeptide proteins from noisy tomographic projections.
method Shape analysis and matrix Lie group actions to deform point clouds to match 2D tomography data.
result Optimal deformations are computed to recover the three-dimensional backbone structure of proteins.
Generative model combines shape and intensity priors for left atrium segmentation.
problem Challenges in segmenting left atrium MRI images due to shape variation and multimodality.
method Generative image model with mixture of Gaussians for shape priors and autoencoders for intensity priors.
result Maximizes posterior probability using a mixture of Gaussians for shape priors and autoencoders for intensity priors.
A novel 3D shape registration method using spectral graph embedding and probabilistic matching.
problem Challenges in 3D shape analysis and registration, especially with large variability.
method Combining spectral graph matching with Laplacian embedding for large graphs, using commute-time embedding and PCA.
result A method to register shapes with different samplings and isometric deformations.
Bayesian method selects important covariates in modal regression.
problem Bayesian modal regression with heavy-tailed responses.
method Expectation-maximization algorithm for parameter estimation; test statistic for variable selection.
result Efficacy of the proposed method in identifying important covariates.
EoS selectively shapes learning, affecting some groups more than others.
problem EoS affects learning differently across the data distribution.
method Branching intervention to enter or exit EoS regime, controlled perturbation to isolate mechanisms.
result EoS redistributes learning, amplifying progress on some groups and suppressing others.
A hierarchical clustering algorithm for data clouds without structure assumptions.
problem Exploring data clouds without making structure assumptions.
method Hierarchical topological clustering algorithm that infers persistence of outliers and clusters of arbitrary shape from data hierarchy.
result The algorithm can provide meaningful clusters in complex datasets.
The paper studies how arm selection in a bandit problem changes with shape constraints.
problem Stochastic Thresholding Bandit Problem under shape constraints.
method Investigation of TBP under four shape constraints: monotonic increasing, unimodal, concave, and fixed.
result Minimax rates for regret vary significantly depending on the shape constraint.
We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal-gamma prior distribution. In this context, we exhibit a closed-form expression o…
Radiomics approach improves cardiac CVD diagnosis from cine-MRI.
problem Inaccurate expert visualization or clinical indices for CVD classification.
method Estimating radiomic features from cine-MRI, feature selection, advanced machine learning.
result Radiomics features correctly classified 100 cases of five cardiac classes.
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the fr…
A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to …
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning…
Within the framework of the cumulative prospective theory of Kahneman and Tversky, this paper considers a continuous-time behavioral portfolio selection problem whose model includes both running and terminal terms in the objective functional. Despite the existence of S-shaped utility functions and probability distortio…
New research reveals how the pretraining distribution affects in-context learning in large language models.
problem Understanding how the pretraining distribution influences in-context learning in large language models.
method Developed a theoretical framework to characterize the relationship between pretraining distribution properties and in-context learning performance.
result Characterized a fundamental trade-off between robust task selection and generalization in ICL due to the pretraining distribution's statistical properties.
Self-training in linear models shows a U-shaped test-risk curve due to signal forgetting and denoising.
problem Understanding the dynamics of iterative self-training in high-dimensional linear regression.
method Derivation of deterministic-equivalent recursions for prediction risk and effective noise, analysis of signal forgetting and denoising effects.
result An optimal early-stopping time is determined, and a U-shaped test-risk curve is observed.
A new method monitors unstructured 3D shapes without registration.
problem Error-prone registration and mesh reconstruction steps in PCD monitoring.
method Intrinsic geometric properties of shapes, using Laplacian and geodesic distances.
result Effective monitoring of defects without registration and mesh reconstruction.
By monitoring the time evolution of the most liquid Futures contracts traded globally as acquired using the Bloomberg API from 03 January 2000 until 15 December 2014 we were able to forecast the S&P 500 index beating the Buy and Hold trading strategy. Our approach is based on convolution computations of 42 of the most …
Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.
problem Goal-based portfolio selection with fixed transaction costs.
method Stochastic Perron's method to show value function is unique viscosity solution to quasi-variational inequalities. Existence of optimal strategy established.
result Optimal trading strategy differs significantly from frictionless case, revealing complex regions and strategies.
2L-FUSE enhances feature sparsity through kernel learning.
problem Sparsity and feature selection in regression tasks.
method 2-Layered kernel machines for learning a shape matrix and feature direction identification.
result Minimal yet informative feature sets are identified without losing predictive performance.
Enhances classifier performance through feature space transformations and model selection.
problem Improving the accuracy of classifiers by reducing complexity.
method Combining feature mapping, prototype selection, and kernel function transformations to transform data into a more convenient distribution.
result Our methods produce competitive classifiers and are statistically different among them.
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exp…
Several representations of geometric shapes involve quotients of mapping spaces. The projection onto the quotient space defines two sub-bundles of the tangent bundle, called the horizontal and vertical bundle. We investigate in these notes the sub-Riemannian geometries of these bundles. In particular, we show for a sel…
Study on investment strategy for agents with periodic preferences and discounting.
problem Investment decisions by agents with periodic S-shaped preferences and present bias.
method Infinite-horizon, continuous-time portfolio selection problem with quasi-hyperbolic discounting.
result Time-consistent planning strategy can be formulated as an equilibrium to a static mean field game.
We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate (PFER). The model is fitted repeatedly to subsampled data…
Estimates true Sharpe ratio of selected assets with various methods.
problem Estimating the true Sharpe ratio of a selected asset with high in-sample ratio.
method Polyhedral lemma, James Stein shrinkage, debiasing, thresholding, empirical Bayes.
result James Stein estimator performs best across various parameter values.
Study examines the excluded area between two-dimensional hard particles, identifying key factors affecting its magnitude.
problem Determining the excluded area between two-dimensional hard particles with various orientations and shapes.
method Used principal component analysis and Monte Carlo simulations to analyze randomly generated non-self-intersecting polygons and star lines.
result The minimum excluded area is achieved when particles are antiparallel, and elongation of the particle shape significantly affects the excluded area.
EPD method accurately captures parameter distributions from RCS data.
problem Limitations of traditional methods in estimating parameter distributions from RCS data.
method EPD method generates synthetic trajectories, estimates parameters, and selects parameters based on discrepancy.
result EPD provides accurate distribution of parameters without data loss.
Investigates portfolio selection with transaction costs and stochastic volatility, using deep learning for computation.
problem Optimal portfolio selection with transaction costs and stochastic volatility.
method Two-factor stochastic volatility model, option-implied utility function, deep learning policy iteration.
result Deep learning method effectively computes optimal investment decisions under transaction costs and stochastic volatility.
RCLA reduces noise in topological data analysis, preserving essential structure.
problem Noise in large datasets obscures topological features in persistent homology.
method Grid-based RCLA integrates data reduction and denoising with a threshold parameter.
result RCLA provides a theoretical guarantee and automatic parameter selection.
FAST optimizes additive segmentation for faster, more interpretable models.
problem Efficiently segmenting and interpreting complex datasets.
method Optimization framework for fast piecewise constant shape functions.
result 2 orders of magnitude faster than state-of-the-art methods.
Adaptive RBF-KAN improves KANs by dynamically adjusting kernel parameters.
problem Efficiently approximating multivariate functions using learnable univariate edge functions.
method Integrates LOOCV-based kernel scale estimation with adaptive kernel learning.
result Adaptive RBF-KAN outperforms fixed kernel KANs on various benchmark functions.
New active learning methods for Gaussian process improve predictive modeling of composite fuselage.
problem Improving predictive modeling of composite fuselage with limited training samples and uncertainties.
method Proposed two new active learning algorithms for Gaussian process considering uncertainties.
result The proposed approach realizes better prediction performance for automatic shape control of composite fuselage.
This paper develops a method to select a reference contract for multi-contract quoting to minimize execution risk.
problem Minimizing execution risk in multi-contract quoting sequences.
method Develops a diagnostic framework using order-flow Hawkes forecasts and CLF to select a stable reference contract.
result Event-history and LOB-state signals offer complementary views for reference-contract selection.
Two models show risk peaks and decreases with feature count.
problem Understanding risk curves in machine learning models with weak features.
method Analyzing two simple data models with least squares/least norm predictors.
result Risk peaks when features are close to sample size, decreases beyond it.
Proposes a new hyperprior and predictive criterion for weakly informative hyperprior in relevance vector machine.
problem Capturing non-homogeneous data structure with limited kernel functions.
method Uses inverse gamma hyperprior with a shape parameter close to zero and a scale parameter not close to zero. Applies multiple kernel method with different widths. Proposes extended predictive information criterion for scale parameter selection.
result Obtains a multiple kernel relevance vector regression model with good predictive accuracy.