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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.

168,695 papers · 148 categories

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15 results for “HAL”

Highly Adaptive Principal Component Regressionstat.ML

PCHAL and PCHAR use principal components to speed up HAL and HAR methods.

problem Computational infeasibility in high dimensions for HAL and HAR.

method Outcome-blind principal component reduction of HAL basis.

result Empirical performance comparable to HAL and HAR, with computational gains.

Principal Component Regression High-dimensional Data HAL HAR Principal Component Analysis

2026-02-11abs ↗pdf ↗

Targeted Highly Adaptive Lasso Minimum Loss Estimation of Target Functionsstat.ME

Proposes a new method for estimating non-pathwise differentiable functional parameters.

problem Estimating dose-response curves for continuous exposure.

method Targeted Highly Adaptive Lasso (HAL) for non-pathwise differentiable functional parameters.

result The Targeted HAL-MLE achieves dimension-free rates up to log(n) factors and outperforms other methods in simulations.

Targeted Lasso Functional Parameter Estimation Dose-Response Curves Smoothness Class Data-Adaptive

2026-07-04abs ↗pdf ↗

Multi-task Highly Adaptive Lassostat.ML

MT-HAL learns features and task associations for multiple tasks with a shared sparse structure.

problem Learning features and task associations for multiple tasks with shared structure.

method Fully nonparametric approach that learns features, samples, and task associations with a shared sparse structure.

result MT-HAL achieves a powerful convergence rate and outperforms other methods across various simulation settings.

multi-task learning sparse structure nonparametric convergence rate feature learning

2023-01-27abs ↗pdf ↗

Hyperactive Learning (HAL) for Data-Driven Interatomic Potentialsphysics.comp-ph

HAL accelerates the generation of training sets for accurate interatomic potentials.

problem Generating accurate and transferable interatomic potentials is time-consuming and requires expert input.

method HAL framework using a physically motivated sampler with a biasing term to drive high uncertainty configurations.

result HAL-generated training databases for alloys and polymers predict macroscopic properties with high accuracy.

interatomic potentials data-driven modeling accelerated sampling alloys polymers

2022-10-09abs ↗pdf ↗

Randomized Nonnegative Matrix Factorizationstat.ML

Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized hierarchical alternating least squares (HALS) algorithm to comput…

2017-11-06abs ↗pdf ↗

Espace de Modules Marques des Surfaces Projectives Convexes de Volume Finimath.GT

This article follow the article {http://hal.archives-ouvertes.fr/hal-00361030/fr/} in which the author characterize the fact of being of finite volume for a convex projective surface. We show here that the moduli space $β_f(Σ_{g,p})$ of the convex projective structure on the surface $Σ_{g,p}$ of genius $g$ with $p$ pun…

2009-10-30abs ↗pdf ↗

Active Learning for Skewed Data Setscs.LG

A new active learning method for skewed data sets.

problem Severe class imbalance and small initial training data in sequential active learning.

method HAL: a hybrid active learning algorithm balancing labeled and unlabeled data.

result HAL makes better choices for labeling points than strong baselines.

active learning skewed data sequential learning class imbalance binary classifier

2020-05-23abs ↗pdf ↗

A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)cs.LG

New method finds basins of attraction without needing system models.

problem Determining basins of attraction (BoA) for nonlinear systems without prior knowledge.

method Hybrid Active Learning (HAL) method combining AST, AL, and DBS.

result Efficiently finds and labels boundary of BoA without model knowledge.

basins of attraction nonlinear systems model-free hybrid active learning sampling method

2020-03-24abs ↗pdf ↗

Possibility Before Utility: Learning And Using Hierarchical Affordancescs.LG

HAL learns hierarchical affordances to prune impossible subtasks, improving reinforcement learning efficiency.

problem Reinforcement learning struggles with complex hierarchical dependency structures.

method HAL learns a model of hierarchical affordances to prune impossible subtasks.

result HAL agents are better at learning complex tasks, navigating stochastic environments, and acquiring diverse skills.

Reinforcement Learning Hierarchical Affordances Efficiency Learning Efficiency Human Learning

2022-03-23abs ↗pdf ↗

f-GRPO and Beyond: Divergence-Based Reinforcement Learning Algorithms for General LLM Alignmentcs.LG

Extends reinforcement learning alignment to scalar rewards, improving math reasoning.

problem Designing reinforcement learning algorithms for general LLM alignment.

method Introduces f-GRPO and f-HAL, estimating f-divergences between reward-aligned and unaligned distributions.

result Improves math reasoning RLVR tasks and mitigates reward hacking.

reinforcement learning alignment scalar rewards f-divergences math reasoning

2026-02-05abs ↗pdf ↗

Accelerating Nonnegative Matrix Factorization Algorithms using Extrapolationmath.NA

In this paper, we propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapola…

2018-05-17abs ↗pdf ↗

Delta Hedging in Financial Engineering: Towards a Model-Free Approachq-fin.PR

Delta hedging, which plays a crucial rôle in modern financial engineering, is a tracking control design for a "risk-free" management. We utilize the existence of trends in financial time series (Fliess M., Join C.: A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory…

2010-05-03abs ↗pdf ↗

Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updatescs.LG

Identifying recurring patterns in high-dimensional time series data is an important problem in many scientific domains. A popular model to achieve this is convolutive nonnegative matrix factorization (CNMF), which extends classic nonnegative matrix factorization (NMF) to extract short-lived temporal motifs from a long …

2019-06-29abs ↗pdf ↗

Nonnegative matrix factorization with side information for time series recovery and predictionstat.ML

Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features). We consider general linear measurement settings, and propose a framework which models non-linear relationships between features …

2017-09-19abs ↗pdf ↗

PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorizationcs.LG

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have b…

2019-04-16abs ↗pdf ↗