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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12 results for “inverse-variance”

Batch Inverse-Variance Weighting: Deep Heteroscedastic Regressioncs.LG

Improves deep learning performance on noisy datasets using inverse-variance weighting.

problem Heteroscedastic regression with varying noise levels.

method Batch Inverse-Variance (BIV) loss function for neural networks.

result Significantly improves network performance on noisy datasets compared to other methods.

heteroscedastic regression inverse-variance weighting deep learning robustness neural networks

2021-07-09abs ↗pdf ↗

The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effectsstat.ME

R-Learning uses inverse-variance weights to estimate treatment effects more accurately.

problem Estimating heterogeneous treatment effects (CATEs) with stable and accurate methods.

method R-Learning with inverse-variance weights (IVWs) for pseudo-outcome regression.

result IVWs improve the stability and accuracy of CATE estimation.

R-Learning Inverse-Variance Weights CATEs Treatment Effects Pseudo-Outcome Regression

2023-07-19abs ↗pdf ↗

An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Modelscs.LG

Analyzes how diffusion models learn, revealing a spectral bias in structure mastery.

problem Understanding the learning dynamics and bias in diffusion models.

method Developed an analytical framework using a Gaussian-equivalence principle to solve gradient-flow dynamics and integrate probability-flow ODEs.

result Exposes a universal inverse-variance spectral law: high-variance structure is mastered faster than low-variance detail.

spectral bias diffusion models learning dynamics inverse-variance weight sharing

2025-03-05abs ↗pdf ↗

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learningcs.LG

Enhances reinforcement learning uncertainty estimation with a generalized Gaussian error model.

problem Inaccurate error representations and compromised uncertainty estimation in conventional uncertainty-aware TD learning.

method Introduces a novel framework for generalized Gaussian error modeling in deep reinforcement learning, incorporating higher-order moments, particularly kurtosis, to improve uncertainty estimation and mitigation.

result Significant performance gains in policy gradient algorithms with the proposed framework.

Reinforcement Learning Temporal Difference Learning Uncertainty Estimation Generalized Gaussian Distribution Aleatoric Uncertainty

2024-08-05abs ↗pdf ↗

DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experimentsstat.ML

DARTS optimizes covariate selection in trials with limited data.

problem Limited budget for high-dimensional pretreatment data.

method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).

result DARTS efficiently concentrates budget on informative features.

covariate selection sequential optimization Thompson sampling causal inference budget-constrained

2026-05-07abs ↗pdf ↗

General Pruning Criteria for Fast SBLstat.ML

The paper analyzes SBL pruning criteria under weakened assumptions.

problem Sparse Bayesian learning hyperparameter divergence and pruning.

method Analyzing marginal likelihood function under weakened Gaussian assumptions.

result Conditions for finite vs infinite hyperparameters lead to F-SBL pruning.

Sparse Bayesian Learning SBL Pruning Marginal Likelihood Hyperparameter Divergence

2025-09-25abs ↗pdf ↗

Robust volatility updates for Hierarchical Gaussian Filteringcs.LG

Improved HGF networks avoid negative precision errors in volatility updates.

problem Negative posterior precision errors in volatility-coupled nodes of HGF networks.

method Introduced a modified quadratic approximation to variational energy.

result Robust update equations across parameter space that track posterior faithfully.

Hierarchical Gaussian Filtering Volatility updates Negative precision Quadratic approximation Lambert W function

2026-05-01abs ↗pdf ↗

Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principlecs.NE

Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data. In this paper, we consider the dimensionality reduction in parameter spaces of binary multivariate distributions. We propose a general Confident-Information-First (CIF) prin…

2013-02-16abs ↗pdf ↗

Ensemble Threshold Calibration for Stable Sensitivity Controlcs.LG

Framework for precise recall control in spatial conflation tasks.

problem Precise recall control in large-scale spatial conflation tasks to avoid downstream analytics failures and excessive manual review.

method End-to-end framework using equigrid bounding-box filter, CSR representation, neural ranker, and inverse-variance weighted ensemble of threshold estimators.

result Achieves exact recall with sub-percent variance over tens of millions of geometry pairs, runs on a single TPU v3 core.

spatial conflation entity matching recall control ensemble threshold calibration sub-percent variance

2025-10-02abs ↗pdf ↗

Contrast-Space Projection for Network Meta-Analysis: An Exact and Invariant Study-Based Decomposition of Direct and Indirect Contributionsstat.ME

Develops exact and invariant study-based decompositions for network meta-analysis.

problem Lack of exact contribution decompositions in network meta-analysis.

method Contrast-space projection formulation of NMA, study-based definition of direct and indirect evidence.

result Exact covariance-aware decompositions of NMA estimator into direct and indirect contributions.

network meta-analysis exact decompositions contrast-space projection study-based evidence forest plots

2026-04-23abs ↗pdf ↗

Beyond De Prado and Cotton: Hierarchical and Iterative Methods for General Mean-Variance Portfoliosq-fin.PM

New methods incorporate alpha signals into portfolio construction, improving performance.

problem Signal-blindness in existing portfolio construction methods.

method Introduces three methods: HRP-

\mu

, HRP-

\Sigma\mu

, and CRISP.

result CRISP at intermediate

\gamma

consistently outperforms other methods.

portfolio construction signal incorporation mean-variance optimization alpha signals Monte Carlo experiments

2026-04-26abs ↗pdf ↗

Instance-Optimal Estimation with Multiple LLM Judges on a Budgetcs.LG

Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.

problem Evaluating LLMs with heterogeneous judges and varying costs and reliability.

method Formalizes and analyzes budgeted heteroskedastic multi-judge estimation, proposing EST-IVWE for practical implementation.

result EST-IVWE matches the oracle IVWE rate up to lower-order terms in the budget and is instance-optimal.

Large Language Models Evaluation Protocols Heterogeneous Judges Budgeted Allocation Minimax Lower Bound

2026-05-22abs ↗pdf ↗