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.
Gradient penalty improves GAN performance by inducing a large-margin classifier.
A fast method estimates group-adaptive elastic net penalties using co-data.
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov style regularization where the regularization penalty articulates the way in which the marginal density should constrain otherwise unrestricted co…
New study reveals a polynomial penalty for adapting to unknown margin parameters in batched nonparametric bandits.
Study improves speaker verification accuracy using angular based embedding learning.
Proposes a gradient-based variable selection method for binary classification in RKHS.
Efficient cross-validation for multi-penalty ridge regression.
Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on -type regularization which accounts for the stability and smoothness of the impulse response to be estimated, these approaches have been shown to be competitive w.r.t classical parametric met…
The paper improves GP regression for sparse sensor data in structural mode shape reconstruction.
A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one…
Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty…
We show that gradient descent on full-width linear convolutional networks of depth converges to a linear predictor related to the bridge penalty in the frequency domain. This is in contrast to linearly fully connected networks, where gradient descent converges to the hard margin linear support vector m…
Risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained under a margin condition in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penal…
Paper reformulates UOT as non-negative penalized linear regression for efficient algorithms.
New findings show a balance between data fit and complexity in kernel hyperparameters.
EDSVM uses elite observations to guide SVM classification.
Enhances UPSA to reduce noise in financial data.
A new algorithm COVA-FC improves subgroup-fair clustering efficiency.
Recent developments in linear system identification have proposed the use of non-parameteric methods, relying on regularization strategies, to handle the so-called bias/variance trade-off. This paper introduces an impulse response estimator which relies on an -type regularization including a rank-penalty derive…
Proposes a fair classification model using robust optimization.
Paper calculates the exact error of LDA models.
GTMs model complex multivariate data with varying conditional independencies.
Generative AI connects to Schrödinger bridge problems with soft constraints for stability.
Improved robustness of machine learning models with controlled Lipschitz constants.
We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric un…
New scalable algorithm for non-negative linear regression with entropy-regularized OT loss.
A new copula, the checkerboard copula, maximizes entropy and preserves dependence.
Framework learns stochastic dynamics from endpoint and intermediate distributions using soft energy constraints.
Covariance graphical lasso applies a lasso penalty on the elements of the covariance matrix. This method is useful because it not only produces sparse estimation of covariance matrix but also discovers marginal independence structures by generating zeros in the covariance matrix. We propose and explore two new algorith…
Novel loss functions improve decision tree learning from noisy data.
The paper explores nonconvex penalties for deep learning regularization.
Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, infections are caused by the interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI pr…
ACFS optimizes spectral risk under decision-dependent uncertainty using adaptive forest sampling.
SIC measures dependency between variables, promoting feature selection.
One-bit measurements widely exist in the real world, and they can be used to recover sparse signals. This task is known as the problem of learning halfspaces in learning theory and one-bit compressive sensing (1bit-CS) in signal processing. In this paper, we propose novel algorithms based on both convex and nonconvex s…
The paper studies robust risk measures with linear penalties under uncertain distributions.
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very limited…
Develops a new method to model overlapping asymmetric datasets effectively.
New sparse penalty improves biclustering for gene expression data.
New approach avoids excess empirical risk in domain generalization.
We study the problem of estimating high-dimensional regression models regularized by a structured sparsity-inducing penalty that encodes prior structural information on either the input or output variables. We consider two widely adopted types of penalties of this kind as motivating examples: (1) the general overlappin…
Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfi…
Curvature penalties improve interpretability of KANs without sacrificing accuracy.
New method reduces bias in sparse Bayesian learning.
We consider a one-period Kyle (1985) framework where the insider can be subject to a penalty if she trades. We establish existence and uniqueness of equilibrium for virtually any penalty function when noise is uniform. In equilibrium, the demand of the insider and the price functions are in general non-linear and remai…
New nonconvex penalty smooths at origin for deep learning.
We consider a problem of data integration. Consider determining which genes affect a disease. The genes, which we call predictor objects, can be measured in different experiments on the same individual. We address the question of finding which genes are predictors of disease by any of the experiments. Our formulation i…
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input or output sides. We consider two widely adopted types of such penalties as our motivating examples: 1) overlapping group lasso penalty, base…