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.

168,742 papers · 148 categories

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13 results for “functional-output”

Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processesstat.ME

Proposes FOAGP for efficient orthogonal effect decomposition of black-box computer experiments.

problem Challenges in sensitivity analysis of black-box computer experiments with complex, nonlinear functional outputs.

method Functional-output orthogonal additive Gaussian process (FOAGP) with conditional orthogonality constraint.

result Demonstrates effectiveness in orthogonal effect decomposition and variance decomposition through simulations and real-world application.

sensitivity analysis orthogonal effect decomposition Gaussian process functional-output black-box experiments

2025-06-15abs ↗pdf ↗

Functional Output Regression with Infimal Convolution: Exploring the Huber and

ε

-insensitive Lossesstat.ML

The paper introduces a new FOR framework using Huber and ε-insensitive losses.

problem Handling outliers and sparsity in functional output regression.

method Proposes a flexible FOR framework with infimal convolution losses and computable algorithms.

result Demonstrates efficiency and effectiveness on synthetic and real-world data.

functional output regression infimal convolution Huber loss ε-insensitive loss outliers

2022-06-16abs ↗pdf ↗

Bayesian Kernel Regression for Functional Datastat.ML

Bayesian kernel regression improves functional output prediction.

problem Functional output regression in supervised learning.

method Kernel methods, leveraging covariance structure within function values.

result Enhanced prediction accuracy and handling of high-dimensional nonlinearity.

Bayesian Kernel methods Functional data Regression Multitask learning

2025-03-17abs ↗pdf ↗

Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variationcs.LG

New approach to handle ranking function variation in zero-shot NAS.

problem Variation in ranking function outputs due to randomness.

method Viewing ranking function output as a random variable and constructing a stochastic ordering.

result Stochastic ordering boosts performance in neural architecture search.

zero-shot NAS ranking function variation stochastic ordering performance metrics

2025-02-27abs ↗pdf ↗

Nonlinear Functional Output Regression: a Dictionary Approachstat.ML

A novel dictionary-based approach for predicting functions.

problem Functional-output regression with non-orthogonal dictionaries.

method Projection learning (PL) with reproducing kernel Hilbert spaces (KPL).

result KPL offers a flexible and computationally efficient solution.

functional regression dictionary learning reproducing kernel Hilbert spaces empirical risk minimization functional square loss

2020-03-03abs ↗pdf ↗

Scalable Training of Inference Networks for Gaussian-Process Modelsstat.ML

Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an alternative approximation that employs stochastic inference networks for a flexible inference. Unfortunately, for such networks, minibatch tra…

2019-05-27abs ↗pdf ↗

Mean Parity Fair Regression in RKHSstat.ML

Proposes a method for fair regression using RKHS.

problem Ensuring fairness in regression models with multiple sensitive attributes.

method Uses reproducing kernel Hilbert space (RKHS) to construct a functional space that satisfies MP fairness.

result Derives a closed-form solution for fair regression that is efficient and interpretable.

fair regression mean parity reproducing kernel Hilbert space functional space sensitive attributes

2023-02-21abs ↗pdf ↗

Scalable Operator Learning via Nyström Approximation With Denoising Applicationsmath.ST

Nyström approximation for scalable operator learning

problem Scalability of operator learning for large datasets

method Nyström subsampling with operator learning

result Minimax-optimal convergence rates for functional outputs

Nyström operator learning scalability kernel methods denoising

2026-06-25abs ↗pdf ↗

SurvSHAP(t): Time-dependent explanations of machine learning survival modelscs.LG

SurvSHAP(t) explains time-dependent survival predictions from machine learning models.

problem Interpreting complex survival models for time-dependent effects.

method SHapley Additive exPlanations (SHAP) adapted for time-dependent survival predictions.

result SurvSHAP(t) detects time-dependent effects and improves variable importance detection.

Survival analysis Machine learning Interpretability SHAP Time-dependent effects

2022-08-23abs ↗pdf ↗

Neural Networks for Scalar Input and Functional Outputstat.ML

Neural network predicts functional responses from scalar inputs.

problem Regression of functional responses with large scalar predictors and nonlinear relationships.

method Transform functional response to finite dimensions, design feed-forward neural network, modify output via objective functions, apply roughness penalty.

result Proposed neural network outperforms conventional methods in multiple scenarios.

neural networks functional regression scalar inputs objective functions roughness penalty

2022-08-10abs ↗pdf ↗

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetrystat.ML

Efficient MCMC sampling in Bayesian neural networks by exploiting symmetries.

problem Challenges in Bayesian inference due to high-dimensional, multi-modal posterior density landscapes.

method Exploiting symmetries in the posterior landscape to restrict the parameter space and derive an upper bound on Monte Carlo chains.

result Efficient sampling is possible, offering a promising path for accurate uncertainty quantification in deep learning.

Bayesian neural networks MCMC sampling symmetry exploitation Monte Carlo methods uncertainty quantification

2023-04-06abs ↗pdf ↗

Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small datasetstat.ML

The paper tackles Bayesian inference with small datasets using manifold learning.

problem Small datasets challenge Bayesian inference with non-Gaussian models.

method Manifold learning and sampling for Bayesian posterior approximation.

result The method effectively samples Bayesian posteriors with non-Gaussian models from small datasets.

Bayesian inference manifold learning small datasets non-Gaussian models manifold sampling

2019-10-28abs ↗pdf ↗

Quantum Extremal Learningquant-ph

Quantum algorithm finds extremal values without direct function access.

problem Finding extremal values of hidden functions without direct access.

method Parametric quantum circuit trained with a trainable quantum feature map.

result Algorithm successfully finds extremal values even with sparse training data.

quantum machine learning extremal learning quantum optimization hidden function sparse data

2022-05-05abs ↗pdf ↗