On-device research index

arXiv research

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.

168,657 papers · 148 categories

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48 results for “RKHS-valued regression”

Conditional Counterfactual Mean Embeddings: Doubly Robust Estimation and Learning Ratesstat.ML

Proposes CCME framework for estimating heterogeneous treatment effects.

problem Estimating heterogeneous treatment effects in complex distributions.

method Embeds conditional distributions into RKHS, develops meta-estimators for CCME.

result Establishes finite-sample convergence rates and double robustness for CCME estimators.

heterogeneous treatment effects conditional counterfactual mean embeddings reproducing kernel Hilbert space double robustness RKHS-valued regression

2026-02-04abs ↗pdf ↗

Recursive Estimation of Conditional Kernel Mean Embeddingsstat.ML

New recursive algorithm estimates conditional kernel mean embeddings in Hilbert space.

problem Estimating conditional distributions in RKHS for supervised learning.

method Recursive algorithm in

L_2

space for conditional kernel mean map.

result Strong

L_2

consistency of recursive estimator proved.

kernel mean embeddings conditional distributions Hilbert space machine learning supervised learning

2023-02-12abs ↗pdf ↗

Near-Optimal Approximations for Bayesian Inference in Function Spacestat.ML

A scalable algorithm approximates Bayesian posteriors in RKHS with improved efficiency.

problem Scalable inference for Bayes posteriors in infinite-dimensional spaces.

method Approximate Langevin diffusion projection onto first M components, using law of total probability and sufficiency assumption.

result The method recovers SVGP as a special case and is provably close to optimal for convex and Lipschitz continuous likelihoods.

Bayesian inference RKHS Langevin diffusion approximation scalability

2025-02-25abs ↗pdf ↗

Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studiocs.LG

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…

2018-04-04abs ↗pdf ↗

Differentially Private Regression with Unbounded Covariatescs.CR

Efficiently private regression for unbounded data.

problem Privacy constraints in regression settings with unbounded covariates.

method Differential privacy techniques on mean and covariance estimation extended to sub-gaussian regime.

result Unbiased estimate of true regression vector learned up to a scaling factor.

differential privacy regression unbounded covariates binary regression linear regression

2022-02-19abs ↗pdf ↗

Robust Regression via Mutivariate Regression Depthmath.ST

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…

2017-02-15abs ↗pdf ↗

A Statistical Learning Approach to Modal Regressionstat.ML

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…

2017-02-20abs ↗pdf ↗

Are Latent Factor Regression and Sparse Regression Adequate?stat.ME

Proposes FARM model combining latent factor and sparse regression.

problem Testing adequacy of latent factor and sparse regression models.

method Factor Augmented sparse linear Regression Model (FARM) with FabTest and ANOVA type tests.

result Model robustness and effectiveness validated through experiments.

Factor Augmented Regression Sparse Regression Latent Factor Test Adequacy De-Biased Test

2022-03-02abs ↗pdf ↗

Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Surveystat.ME

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

problem Reducing dimensionality in high-dimensional data.

method Involves both statistical and machine learning approaches, covering inverse and forward regression methods.

result Supervised Kernel Dimension Reduction is equivalent to supervised PCA.

Sufficient Dimension Reduction Inverse Regression Forward Regression Kernel Dimension Reduction Principal Hessian Directions

2021-10-18abs ↗pdf ↗

A selective review of sufficient dimension reduction for multivariate response regressionstat.ME

This paper reviews SDR methods for multivariate response regression.

problem Handling sufficient dimension reduction for multivariate response regression.

method Characterizes SDR estimators as inverse or forward regression methods.

result Pooled marginal, projective resampling, distance-based, ordinary least squares, partial least squares, and semiparametric SDR estimators are discussed.

sufficient dimension reduction multivariate response inverse regression forward regression estimators

2022-02-02abs ↗pdf ↗

Semi-supervised learning for linear extremile regressionstat.ME

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

problem Challenges in high-dimensional extremile regression due to data sparsity and overfitting.

method Proposes semi-supervised learning for linear extremile regression, achieving

\sqrt{n}

-consistency.

result Demonstrates improved estimation efficiency and performance in high-dimensional settings.

semi-supervised learning linear extremile regression high-dimensional data quantile regression extremile regression

2025-07-02abs ↗pdf ↗

A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programmingstat.ML

Improves logistic regression performance with nonconvex programming.

problem Stochastic generalized linear regression with chance constraints.

method Nonconvex programming techniques, clustering, quantile estimation.

result Over 1 to 2 percent improvement in model performance.

logistic regression stochastic optimization nonconvex programming chance constraints performance metrics

2024-01-16abs ↗pdf ↗

H

-Consistency Guarantees for Regressioncs.LG

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

problem Improving

H

-consistency bounds for regression analysis.

method Generalized theorems and novel

H

-consistency bounds for various surrogate loss functions.

result Derives principled surrogate losses for adversarial regression.

$H$-consistency regression surrogate loss adversarial regression Huber loss

2024-03-28abs ↗pdf ↗

Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional datacs.LG

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

problem Efficient probabilistic classification in high-dimensional data with logistic regression.

method Developed a prevalidated ridge regression model that matches logistic regression's performance but is more computationally efficient.

result Prevalidated ridge regression achieves similar classification error and log-loss to logistic regression for high-dimensional data.

ridge regression logistic regression high-dimensional data classification error log-loss

2024-01-28abs ↗pdf ↗

Partial Trace Regression and Low-Rank Kraus Decompositioncs.LG

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

problem Learning matrix-to-matrix mappings from data.

method Partial trace regression model, leveraging quantum information theory.

result Relevance demonstrated in matrix-to-matrix regression and positive semidefinite matrix completion.

trace regression partial trace quantum information matrix completion positive semidefinite

2020-07-02abs ↗pdf ↗

Meta Optimality for Demographic Parity Constrained Regression via Post-Processingstat.ML

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

problem Regression under demographic parity constraints.

method Meta-theorems and post-processing methods.

result Fair minimax optimal regression can be achieved through post-processing.

fairness regression demographic parity post-processing meta-theorems

2025-06-16abs ↗pdf ↗

Asymptotic Optimism of Random-Design Linear and Kernel Regression Modelsstat.ML

We analyzed optimism in linear and kernel regression models.

problem Understanding predictive complexity in regression models.

method Derived closed-form asymptotic optimism for linear and kernel regression models.

result Scaled optimism is a useful measure for model complexity.

optimism regression models kernel ridge regression neural networks predictive complexity

2025-02-18abs ↗pdf ↗

Nonlinear Regression Analysis Using Multi-Verse Optimizercs.LG

A new optimizer, MVO, improves nonlinear regression performance.

problem Finding optimal coefficients in nonlinear regression models.

method Multi-Verse Optimizer (MVO) compared to Particle Swarm Optimizer (PSO).

result MVO statistically outperforms PSO in 10 nonlinear regression problems.

nonlinear regression meta-heuristic Multi-Verse Optimizer Particle Swarm Optimizer optimization

2020-04-28abs ↗pdf ↗

Brenier Isotonic Regressionstat.ML

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

problem Enforcing cyclic monotonicity in multi-output regression.

method Leverage Kantorovich's optimal transport to find cyclically monotone couplings.

result Brenier isotonic regression outperforms baselines in probability calibration.

Isotonic regression Optimal transport Probability calibration Generalized linear models

2026-03-11abs ↗pdf ↗

Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Featurescs.LG

We simplify complex regression coefficients using linearization and feature comparison.

problem Interpreting high-dimensional regression coefficients from nonlinear responses.

method Developed a linearization method to derive feature coefficients and compare them with regression coefficients.

result Shows how regression coefficients relate to linearized feature coefficients and how they change under regularization.

high-dimensional regression linearization feature comparison regression coefficients nonlinear responses

2024-11-18abs ↗pdf ↗

Fair Regression under Demographic Parity: A Unified Frameworkstat.ME

Unified framework for fair regression under demographic parity.

problem Ensuring fairness in regression tasks subject to demographic parity constraints.

method Proposes a unified framework applicable to various regression tasks with a broad spectrum of loss functions, derived a novel characterization of the fair risk minimizer, and established theoretical consistency and convergence rates.

result Effective minimization of risk while satisfying fairness constraints across various regression settings.

fair regression demographic parity risk minimization regression tasks loss functions

2026-01-15abs ↗pdf ↗

A Locally Adaptive Interpretable Regressionstat.ML

Locally adaptive interpretable regression improves linear regression's predictability.

problem Linear regression's predictability is limited; it lacks adaptability.

method Locally adaptive interpretable regression (LoAIR) uses neural networks to predict percentile of a Gaussian distribution for regression coefficients.

result LoAIR achieves comparable or better predictive performance than state-of-the-art baselines.

interpretable regression locally adaptive neural networks predictability linear regression

2020-05-07abs ↗pdf ↗

A Statistical Learning Assessment of Huber Regressionmath.ST

Huber regression assessed for robustness in statistical learning.

problem Understanding Huber regression in nonparametric statistical learning.

method Assessment from statistical learning perspective, focusing on risk consistency, adaptive tuning, and convergence rates.

result Huber regression can be asymptotically mean regression calibrated under

(1+ε)

-moment conditions, justifying its robustness.

Huber regression statistical learning risk consistency adaptive tuning mean regression

2020-09-27abs ↗pdf ↗

Least angle and

\ell_1

penalized regression: A reviewstat.ME

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…

2008-02-07abs ↗pdf ↗

On Coresets For Regularized Regressioncs.LG

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

problem Analyzing coresets for regularized regression problems.

method Examined coresets for ridge regression and proposed a modified lasso problem.

result No coreset for regularized regression can be smaller than the unregularized version when

r eq s

coresets regularized regression lasso sparsity multi-response regression

2020-06-09abs ↗pdf ↗

Uniform Consistency in Nonparametric Mixture Modelsmath.ST

Study uniform consistency in nonparametric mixture models and mixed regression.

problem Uniform consistency in nonparametric mixture models and mixed regression models.

method Construct uniformly consistent estimators under general conditions, develop novel technical tools.

result Prove uniform consistency results for nonparametric mixtures and mixed regression models.

uniform consistency nonparametric mixture models mixed regression regression functions error distributions

2021-08-31abs ↗pdf ↗

A Note On Nonlinear Regression Under L2 Losscs.LG

We find a convex model for traditional nonlinear regression under L2 loss.

problem Nonlinear regression under L2 loss with non-convex optimization.

method Showed a convex nonlinear regression model for least squares problem.

result Existence of a convex model simplifies training complex systems.

nonlinear regression convex optimization least squares L2 loss

2023-03-30abs ↗pdf ↗

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledgestat.ML

Collider regression improves predictive performance in regression tasks.

problem Discarding prior causal knowledge in regression tasks.

method Collider regression framework incorporating probabilistic causal knowledge from collider structures.

result Proves positive generalization benefit and provides closed-form estimators.

regression causal inference colliders machine learning predictive performance

2023-01-26abs ↗pdf ↗

Dimensionally Constrained Symbolic Regressionstat.ML

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

2011-06-20abs ↗pdf ↗

Gaussian Process Model for Estimating Piecewise Continuous Regression Functionsstat.ME

New GP model estimates piecewise continuous functions.

problem Piecewise continuous regression functions in scientific and engineering applications.

method Local Gaussian process model with partitioned local data and joint estimation of boundaries.

result Superior performance over conventional GP models in estimating piecewise regression functions.

Gaussian process piecewise regression maximum likelihood local linear boundary regression estimate

2021-04-13abs ↗pdf ↗

Piecewise linear regression and classificationcs.LG

PARC uses piecewise linear predictors for regression and classification.

problem Multivariate regression and classification problems.

method Alternates between ridge and softmax regression, and cluster assignment based on accuracy and separability.

result Converges to a local minimum in a finite number of steps.

Piecewise linear Regression Classification Block-coordinate descent Ridge regression

2021-03-10abs ↗pdf ↗

Theoretical and Practical Analysis of Fréchet Regression via Comparison Geometrystat.ML

This work analyzes Fréchet regression using comparison geometry, providing theoretical and practical insights.

problem Analyzing data on complex structures like manifolds and graphs.

method Theoretical analysis through comparison geometry, focusing on existence, uniqueness, and stability of the Fréchet mean.

result Key results on the existence, uniqueness, and stability of the Fréchet mean, along with statistical guarantees for nonparametric regression.

Fréchet regression comparison geometry manifolds graphs statistical guarantees

2025-02-04abs ↗pdf ↗

Dimensionality Reduction and Wasserstein Stability for Kernel Regressionstat.ML

Study on reducing dimensionality in high-dimensional regression with kernel methods and stability analysis.

problem Analyzing errors in high-dimensional regression with dimensionality reduction and kernel regression.

method Derive a stability result for kernel regression with Wasserstein distance and apply it to PCA to deduce convergence rates.

result Two-step procedure yields useful convergence rates in semi-supervised settings.

dimensionality reduction kernel regression Wasserstein distance principal component analysis convergence rates

2022-03-17abs ↗pdf ↗

Ensembles of Probabilistic Regression Treesstat.ML

Paper studies ensemble probabilistic regression trees for smooth approximations.

problem Smooth approximations of regression functions.

method Ensemble versions of probabilistic regression trees.

result Ensemble probabilistic regression trees are consistent and perform well.

probabilistic regression trees ensemble methods smooth approximations regression problems random forests

2024-06-20abs ↗pdf ↗

Nonparametric modal regressionstat.ME

Modal regression estimates the local modes of the distribution of $Y$ given $X=x$ , instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of …

2014-12-04abs ↗pdf ↗

pGMM Kernel Regression and Comparisons with Boosted Treesstat.ML

pGMM kernel outperforms ordinary ridge regression and RBF kernel ridge regression without tuning.

problem Comparing pGMM kernel regression with other ridge regression methods.

method Implemented and compared pGMM kernel regression with ordinary ridge regression and RBF kernel ridge regression.

result pGMM kernel performs well without tuning and can match boosted trees with parameter tuning.

pGMM kernel ridge regression boosted trees kernel methods machine learning

2022-07-18abs ↗pdf ↗

Tensor Regressionstat.ML

Tensor Regression tackles high-dimensional data analysis.

problem Challenges in traditional data representation methods for high-dimensional data.

method Systematic study and analysis of tensor-based regression models.

result Provides solutions for specific regression tasks with multiway data.

Tensor Regression High Dimensional Data Neuroimaging Computer Vision

2023-08-22abs ↗pdf ↗

Demographic parity in regression and classification within the unawareness frameworkstat.ML

The paper explores fair regression and classification under demographic parity constraints.

problem Ensuring fairness in regression and classification models under demographic parity constraints.

method Characterizes the optimal fair regression function using a barycenter problem with optimal transport costs and studies the connection between fair classification and regression.

result The optimal fair regression function is derived from the solution to a barycenter problem with optimal transport costs, and the optimal fair cost-sensitive classifiers can be derived by applying thresholds to this function.

fairness regression classification demographic parity barycenter problem

2024-09-04abs ↗pdf ↗

Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothingcs.LG

VIR model improves regression accuracy and uncertainty estimation for imbalanced data.

problem Imbalanced regression datasets lead to poor model accuracy and uncertainty estimation.

method VIR model uses probabilistic smoothing and reweighting to estimate latent representations and uncertainty.

result VIR model outperforms state-of-the-art models in accuracy and uncertainty estimation.

imbalanced regression uncertainty quantification probabilistic smoothing variational autoencoder

2023-06-11abs ↗pdf ↗

New Insights into Learning with Correntropy Based Regressioncs.LG

New insights into correntropy-based regression reveal robustness and unified approaches.

problem Learning robust regression functions under additive noise.

method Minimum distance estimation and conditional mean, mode, median functions.

result Unified approach to conditional mean, mode, and median functions.

correntropy regression robustness minimum distance conditional mean

2020-06-19abs ↗pdf ↗

Risk Bounds For Distributional Regressionstat.ML

The study establishes risk bounds for distributional regression estimators.

problem Estimating distributional regression models with nonparametric methods.

method Theoretical bounds for CRPS and MSE are derived for convex and non-convex constraints.

result Theoretical risk bounds are validated through experiments on simulated and real data.

distributional regression risk bounds nonparametric methods CRPS MSE

2025-05-14abs ↗pdf ↗

Private Sketches for Linear Regressioncs.LG

Private sketches protect linear regression data privacy.

problem Protecting sensitive information in linear regression.

method Release private sketches of datasets, compute approximate solutions.

result Private sketches maintain good approximation guarantees to the original problem.

differential privacy linear regression sketching private computation approximate solutions

2025-11-10abs ↗pdf ↗

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stoppingstat.ML

Efficiently performs robust and sparse kernel regression.

problem Robust and sparse kernel regression.

method Sign gradient descent and early stopping.

result Sign gradient descent achieves robust and sparse kernel regression efficiently.

kernel regression robustness sparsity gradient descent early stopping

2023-06-29abs ↗pdf ↗

Mixed Regression via Approximate Message Passingstat.ML

New AMP algorithm estimates signals and latent variables in mixed regression models.

problem Estimating signals and latent variables in mixed regression models.

method Approximate Message Passing (AMP) algorithm for matrix GLM.

result State evolution recursion and optimal denoising functions for precise error minimization.

Approximate Message Passing Mixed Linear Regression Max-Affine Regression State Evolution Signal Estimation

2023-04-05abs ↗pdf ↗

Regression-based projection for learning Mori-Zwanzig operatorsmath.DS

Regression learns Mori-Zwanzig operators for dynamical systems.

problem Learning Mori-Zwanzig operators for complex dynamical systems.

method Statistical regression to extract Markov and memory operators.

result Regression models improve learning of memory-dependent corrections.

Mori-Zwanzig data-driven dynamical systems regression memory operators

2022-05-10abs ↗pdf ↗

Anchor Data Augmentationcs.LG

ADA augments data using AR replicas for robust regression.

problem Improving robustness in nonlinear over-parametrized regression.

method Extends Anchor regression (AR) for data augmentation, using replicas of modified samples.

result ADA provides more robust regression predictions compared to state-of-the-art solutions.

data augmentation regression nonlinear over-parametrized Anchor regression

2023-11-12abs ↗pdf ↗

Simple Transferability Estimation for Regression Taskscs.LG

Simple methods improve regression transferability estimation.

problem Estimating how well regression models transfer between tasks.

method Two simple, computationally efficient approaches based on negative regularized mean squared error.

result Significantly outperform existing methods in accuracy and efficiency.

regression transferability estimation deep learning linear regression

2023-12-01abs ↗pdf ↗

Linearized Binary Regressionstat.ML

Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established…

2018-02-01abs ↗pdf ↗