arXiv research

Shape-constrained symbolic regression improves model extrapolation with prior knowledge.

Paper tackles multivariate shape-constrained convex regression problems.

Paper proposes a shape-constrained approach to distributionally robust learning.

HyCNNs improve convex function learning and optimal transport.

Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The first method can be applied to any pre-trained prediction rule, while the second method deals specifically w…

Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$. While this problem is well understood both statistically and computationally, much l…

New algorithms reduce rejection sampling complexity for shape-constrained distributions.

Brenier isotonic regression extends multi-output isotonic regression using optimal transport.

New algorithms solve large-scale convex regression problems.

Functional BART adds shape priors to Bayesian tree regression for better curve fitting.

ICCNLS models complex relationships as convex and concave components.

Proposes a tuning-free dynamic pricing method for linear valuation models.

Paper tackles hard shape constraints in kernel machines.

We consider $N$-way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. Our method, Shape Constrained Tensor Decomposition (SCTD) is based upon the CANDECOMP/PARAFAC (CP) decomposition which produces $r$-rank appr…

A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.

ecpc R-package improves high-dimensional prediction with co-data.

We present a fully nonparametric method to estimate the value function, via simulation, in the context of expected infinite-horizon discounted rewards for Markov chains. Estimating such value functions plays an important role in approximate dynamic programming and applied probability in general. We incorporate "soft in…

In stochastic portfolio theory, a relative arbitrage is an equity portfolio which is guaranteed to outperform a benchmark portfolio over a finite horizon. When the market is diverse and sufficiently volatile, and the benchmark is the market or a buy-and-hold portfolio, functionally generated portfolios introduced by Fe…

Given a matrix the seriation problem consists in permuting its rows in such way that all its columns have the same shape, for example, they are monotone increasing. We propose a statistical approach to this problem where the matrix of interest is observed with noise and study the corresponding minimax rate of estimatio…

Study minimax risk of score estimation for log-concave distributions.

A universal method for hypothesis tests and confidence sets without regularity conditions.

This paper is devoted to the application of B-splines to volatility modeling, specifically the calibration of the leverage function in stochastic local volatility models and the parameterization of an arbitrage-free implied volatility surface calibrated to sparse option data. We use an extension of classical B-splines …

Estimates self- and cross-impact concavity and decay patterns in financial markets.

Dynamic pricing policy converges to Nash equilibrium with low regret.

Venn diagrams are a graphical way to represent a set system. Each of the n sets is represented by a simple closed curve. The n curves subdivide the plane into 2^n open connected regions, each of which represents the intersection of its containing curves' sets. For example, two overlapping circles can divide the plane i…

The paper proposes a method to align AI models using conformal risk control.

Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine…

Efficiently private regression for unbounded data.

This paper studies robust regression in the settings of Huber's $ε$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $ε$-contamination models for various regression problems including nonpa…

This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially moda…

Proposes FARM model combining latent factor and sparse regression.

Survey of SDR methods for high-dimensional regression and embedding.

This paper reviews SDR methods for multivariate response regression.

Paper introduces semi-supervised linear extremile regression for high-dimensional data.

Improves logistic regression performance with nonconvex programming.

Study improves $H$-consistency bounds for regression analysis.

Prevalidated ridge regression simplifies logistic regression for high-dimensional data.

Introduces a new model for mapping matrices to matrices, subsuming linear regression.

Meta-theorems validate fair regression algorithms under demographic parity constraints.

We analyzed optimism in linear and kernel regression models.

A new optimizer, MVO, improves nonlinear regression performance.

We simplify complex regression coefficients using linearization and feature comparison.

Unified framework for fair regression under demographic parity.

Huber regression assessed for robustness in statistical learning.

Locally adaptive interpretable regression improves linear regression's predictability.

The paper improves SVR with linear constraints for better model properties.

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO ($\ell_1$-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has…

We analyze coresets for regularized regression problems and propose a modified lasso that yields smaller coresets.