arXiv research

Develops flexible algorithms for estimating heterogeneous treatment effects.

Study derives a limit functional for Willmore graphs with curvature penalization.

We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size o…

Improves stability of actor-critic methods by penalizing TD error.

The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…

Paper addresses covariate shift in deep learning regression models.

In order to identify important variables that are involved in making optimal treatment decision, Lu et al. (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of ex…

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $κ$ predominant classes using the labeled training examples such that the proportion of t…

Adam's hyperparameters implicitly regularize solutions, penalizing or impeding loss gradients' norms.

We introduce a new probabilistic method for solving a class of impulse control problems based on their representations as Backward Stochastic Differential Equations (BSDEs for short) with constrained jumps. As an example, our method is used for pricing Swing options. We deal with the jump constraint by a penalization p…

ConvSCCS model detects rare adverse drug reactions from EHRs.

We introduce a computationally effective algorithm for a linear model selection consisting of three steps: screening--ordering--selection (SOS). Screening of predictors is based on the thresholded Lasso that is l_1 penalized least squares. The screened predictors are then fitted using least squares (LS) and ordered wit…

New method estimates neuronal connectivity from partially observed data.

We consider the problem of sparse estimation in a factor analysis model. A traditional estimation procedure in use is the following two-step approach: the model is estimated by maximum likelihood method and then a rotation technique is utilized to find sparse factor loadings. However, the maximum likelihood estimates c…

The MM algorithm improves robust penalized estimation for outlier-contaminated data.

The paper develops a method to learn SDE drift functions from sparse, noisy data.

We propose a new framework for deriving screening rules for convex optimization problems. Our approach covers a large class of constrained and penalized optimization formulations, and works in two steps. First, given any approximate point, the structure of the objective function and the duality gap is used to gather in…

Equivalence found between algorithmic regularization and convex penalization for convex losses.

Improved asset allocation strategies using penalized quantile regression.

New method speeds up change-point detection in data sequences.

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamenta…

We develop a maximum penalized quasi-likelihood estimator for estimating in a nonparametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estimators. After developing a numerical scheme for computing the maximizer of the penalized maximum quasi-likelihood function…

Paper introduces structured sparsity estimators for Generalized Linear Models.

Paper estimates differences in multi-attribute Gaussian graphical models using non-convex penalties.

A new algorithm speeds up sparse-penalized quantile regression solving non-convex penalties.

In this paper we study nonconvex penalization using Bernstein functions. Since the Bernstein function is concave and nonsmooth at the origin, it can induce a class of nonconvex functions for high-dimensional sparse estimation problems. We derive a threshold function based on the Bernstein penalty and give its mathemati…

A new Branch-and-Bound solver tackles L0-penalized problems with flexible loss functions.

In this paper we study nonconvex penalization using Bernstein functions whose first-order derivatives are completely monotone. The Bernstein function can induce a class of nonconvex penalty functions for high-dimensional sparse estimation problems. We derive a thresholding function based on the Bernstein penalty and di…

The paper develops a deep neural network estimator for weakly dependent processes with various loss functions.

CDEP improves deep learning model accuracy by penalizing explanations.

New CT image reconstruction method reduces X-ray dose while improving image quality.

This paper introduces a gradient analysis framework to improve language model performance by rewarding good examples and penalizing bad ones.

XCAN uses cross-product penalization for sparse matrix factorization.

Develops a flexible model for regime transitions in time series data.

Label noise in SGD helps converge to flatter minima.

We develop a first order expansion for convex penalized estimators in high-dimensional regression.

In this paper, we propose a one-pass algorithm on MapReduce for penalized linear regression \[f_λ(α, β) = \|Y - α\mathbf{1} - Xβ\|_2^2 + p_λ(β)\] where $α$ is the intercept which can be omitted depending on application; $β$ is the coefficients and $p_λ$ is the penalized function with penalizing parameter $λ$. $f_λ(α, β…

Deep Penalty Method solves high-dimensional optimal stopping problems using deep learning.

The paper develops adaptive deep learning methods for nonlinear time series models.

Study dynamic risk measures with distributional uncertainty using optimal transport.

New method for inference on strongly identified functionals even when nuisance functions are weakly identified.

This paper deals with sparse feature selection and grouping for classification and regression. The classification or regression problems under consideration consists in minimizing a convex empirical risk function subject to an $\ell^1$ constraint, a pairwise $\ell^\infty$ constraint, or a pairwise $\ell^1$ constraint. …

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

Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the presence of many noise variables may mask underlying clustering structures. Hence removing noise variables via variable selection is necessary…

Improved convergence rates for MLE in mixture models using penalized log-likelihood.

Paper uses neural networks to solve complex transport problems.

In this paper the robust utility maximization problem for a market model based on Lévy processes is analyzed. The interplay between the form of the utility function and the penalization function required to have a well posed problem is studied, and for a large class of utility functions it is proved that the dual probl…

Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the l1-norm of the parameter of interest. However, other penalization terms have proven to have…