arXiv research

This paper proposes a new method for GLM estimation using distance penalties to handle constraints.

The paper studies robust risk measures with linear penalties under uncertain distributions.

Gradient penalty improves GAN performance by inducing a large-margin classifier.

The paper uses distance covariance to improve fairness in machine learning models.

New method approximates sampling from smooth potential distributions using a vanishing penalty.

Study improves estimation of functions from noisy data using convex penalties.

We investigate a robust penalized logistic regression algorithm based on a minimum distance criterion. Influential outliers are often associated with the explosion of parameter vector estimates, but in the context of standard logistic regression, the bias due to outliers always causes the parameter vector to implode, t…

This study proves local stability of SGP μ-WGAN and shows penalizing data or sample manifold is key.

Wasserstein GANs with Gradient Penalty compute a different optimal transport problem called congested transport.

Stochastic version of proximal distance algorithm analyzed and validated.

We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperfor…

In this paper we propose and study a family of sparsity-inducing penalty functions. Since the penalty functions are related to the kinetic energy in special relativity, we call them \emph{kinetic energy plus} (KEP) functions. We construct the KEP function by using the concave conjugate of a $χ^2$-distance function and …

We investigate properties of estimators obtained by minimization of U-processes with the Lasso penalty in high-dimensional settings. Our attention is focused on the ranking problem that is popular in machine learning. It is related to guessing the ordering between objects on the basis of their observed predictors. We p…

Unified analysis of multi-attribute graph learning with non-convex penalties.

Energy distance measures feature heterogeneity in federated learning.

Algorithm minimizes loss and constraint violations in online convex optimization with smooth penalties.

New method prevents gradient attenuation in Lipschitz constrained convolutional networks.

Study extends DRO with IPMs, linking robustness to regularization and GANs.

Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications. However, this framework has two main drawbacks: (i) Wasserstein-1 (or Earth-Mover) distance is restrictive such that WGANs cannot always fit data geometry well; (ii) It …

Study improves speaker verification accuracy using angular based embedding learning.

Proposes a method to incorporate prior domain knowledge into hierarchical clustering.

Physics: Similar long-distance properties can mask vastly different short-distance metrics.

Generative adversarial networks (GANs) have received a tremendous amount of attention in the past few years, and have inspired applications addressing a wide range of problems. Despite its great potential, GANs are difficult to train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al. 2017b; and Gu…

Random Fourier features model reconstructs wind fields from sparse measurements.

Study robust distribution estimation with Wasserstein distance, achieving optimal risk.

Generative models create paintings that match training data.

New algorithms improve sampling from constrained distributions.

Unified framework for multi-user bandits using Laplacian kernels.

New privacy mechanism for user-level discrete distributions with reduced penalty.

Two new data synthesizers generate multidimensional time series for analysis.

TRACE analyzes risk changes in models trained on shifted data.

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton's metho…

The paper explores nonconvex penalties for deep learning regularization.

Optimal stopping strategy for a Lévy process near its supremum.

Study examines insider trading with penalties, finding optimal penalties increase quickly for small orders.

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…

This paper presents a unified framework for smooth convex regularization of discrete optimal transport problems. In this context, the regularized optimal transport turns out to be equivalent to a matrix nearness problem with respect to Bregman divergences. Our framework thus naturally generalizes a previously proposed …

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…

Paper introduces a new SVR model using a combined reward and penalty loss function.

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…

Curvature penalties improve interpretability of KANs without sacrificing accuracy.

New method reduces bias in sparse Bayesian learning.

PPO-B improves sampling efficiency by using a logarithmic barrier method.

New nonconvex penalty smooths at origin for deep learning.

Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to …

Optimizes decisions in time-varying distributions using online stochastic methods and Wasserstein distance.