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.

168,695 papers · 148 categories

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9 results for “data-restricted”

Balancing Fairness and Accuracy in Data-Restricted Binary Classificationcs.LG

Proposes a framework for balancing fairness and accuracy in data-restricted binary classification.

problem Balancing fairness and accuracy in applications with data restrictions.

method Directly analyzes the optimal Bayesian classifier's behavior under different data-restricting scenarios, formulating convex optimization problems.

result Demonstrates how accuracy of a Bayesian classifier is affected by fairness constraints in various data-restricting scenarios.

fairness accuracy binary classification data-restricted Bayesian classifier

2024-03-12abs ↗pdf ↗

The characteristic gluing problem for the Einstein equations and applicationsgr-qc

Paper solves Einstein vacuum equations gluing problem with applications.

problem Solving characteristic gluing problem for Einstein vacuum equations.

method Codimension-10 gluing construction, relating charges to ADM energy, and localized version.

result Asymptotically flat data can be glued to Kerr spacetime with 10 charges.

Einstein equations gluing problem Kerr spacetime ADM energy localized gluing

2021-07-06abs ↗pdf ↗

Evaluating Aleatoric Uncertainty via Conditional Generative Modelscs.LG

New method uses generative models to estimate aleatoric uncertainty without strict data restrictions.

problem Estimating aleatoric uncertainty with limited data distribution or dimensionality.

method Conditional generative models and two metrics for measuring distributional discrepancies.

result Metrics accurately measure conditional distributional discrepancies and train competitive models.

aleatoric uncertainty conditional generative models distributional discrepancies machine learning

2022-06-09abs ↗pdf ↗

Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracyq-fin.PM

Double descent in portfolio optimization shows improved performance with complexity, then declines, due to overfitting.

problem Improving portfolio optimization performance with model complexity.

method Investigates the relationship between model complexity and out-of-sample performance in mean-variance portfolio optimization.

result Performance of low-dimensional models initially improves with complexity but declines due to overfitting. High-dimensional models show double ascent Sharpe ratio curve.

portfolio optimization double descent mean-variance model complexity overfitting

2024-11-28abs ↗pdf ↗

Reflected diffusion models adapt to low-dimensional datamath.ST

The paper analyzes reflected diffusion models on hypercube data.

problem Challenges in modeling bounded domains with low-dimensional data.

method Employed an infinite series expansion of transition densities to bound the score function and its approximation.

result Established convergence rates for generative algorithm adapting to intrinsic dimensionality.

reflected diffusion models bounded domains low-dimensional data intrinsic dimension Wasserstein distance

2026-03-25abs ↗pdf ↗

Off-policy Bandits with Deficient Supportcs.LG

Contextual bandit methods fail with deficient support data.

problem Learning from support-deficient data in contextual bandits.

method Three approaches to IPS-based learning: action space restriction, reward extrapolation, and policy space restriction.

result Systematic analysis and empirical evaluation of approaches to IPS-based learning.

contextual bandits off-policy learning support deficiency IPS weighting reward extrapolation

2020-06-16abs ↗pdf ↗

Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Ratemath.OC

Paper analyzes noisy low-rank matrix optimization, improving RIP bounds and convergence rates.

problem Noisy low-rank matrix optimization with general objective functions.

method Develops new mathematical framework and proves convergence rate under RIP condition.

result Any spurious local solution is close to ground truth when RIP constant is less than 1/3.

Low-rank matrix optimization Noisy data Restricted Isometry Property (RIP)Convergence rate Local minima

2022-03-08abs ↗pdf ↗

Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learningecon.EM

Study uses contextual bandits to optimize charity exposure in donation solicitation.

problem Optimizing charity exposure in donation solicitation using survey responses.

method Adaptive experiment design to balance cumulative regret minimization and simple regret minimization.

result Adaptive experimentation yields better policy learning outcomes than uniform randomization.

Charitable Giving Contextual Bandits Donation Solicitation Policy Learning Cumulative Regret

2022-11-22abs ↗pdf ↗

The Limit Order Book Recreation Model (LOBRM): An Extended Analysisq-fin.TR

LOBRM model recreates limit order books from trade and quote data.

problem Lack of LOB data and limitations in LOBRM model.

method Extended LOBRM with time-weighted z-score standardization and exponential decay kernel, conducted in chronological order.

result LOBRM with decay kernel outperforms traditional models and module ensembling is effective.

LOB recreation TAQ data time-weighted z-score exponential decay kernel module ensembling

2021-07-01abs ↗pdf ↗