Empirical median performs well in estimating location with varying scales.
problem Estimating location with varying scales in data.
method Analysis of empirical median as an estimator.
result Matching upper and lower bounds on estimation error.
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.
169,051 papers · 148 categories
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48 results for “robust location estimates”
Paper proposes MWDE for estimating finite location-scale mixtures.
problem Estimating finite location-scale mixtures using MLE is problematic.
method Investigates minimum Wasserstein distance estimators (MWDE).
result MWDE is consistent and provides a numerical solution.
New algorithm optimizes margin distribution in binary classifiers.
problem Optimizing margin distribution in binary classifiers.
method Proposes an algorithm that searches the hypothesis space to ensure a pre-set margin level is a robust estimator of the margin location.
result Empirical tests show the method is effective and promising for classification.
Study minimax robustness in statistical estimation under Wasserstein contamination.
problem Adversarial perturbations in statistical data.
method Developed minimax theory for losses under Wasserstein- contaminations.
result Exact minimax risk identified for joint contaminations in location estimation and prediction in linear regression.
Paper proposes robust estimators for GANs under Wasserstein contamination.
problem Robust estimation of distributions under contamination.
method Wasserstein GAN-based estimators for location, covariance, and regression.
result Proposed estimators are minimax optimal in many scenarios.
Robust deep neural networks estimate multi-dimensional functional data robustly.
problem Estimating location function from multi-dimensional functional data robustly.
method Deep neural networks with ReLU activation, robust to outliers and model misspecification.
result Uniform convergence rates for robust deep neural network estimators.
The paper examines how to make multivariate estimators robust against adversarial data modifications.
problem Adversarial robustness of multivariate -Estimators.
method Adversarial Influence Function (AIF) to measure robustness; optimal modification strategy and AIF characterization; optimal -estimator design.
result Characterization of optimal -estimator with smallest AIF for joint location and scale estimation.
KMRCD detects outliers in non-elliptical data using kernel trick.
problem Outlier detection in non-elliptical data.
method KMRCD estimator that uses kernel trick to compute robust covariance matrix in a feature space.
result KMRCD performs well in simulations and real-life data.
Develops privacy-preserving multivariate median estimation methods.
problem Lack of rigorous privacy guarantees for robust multivariate location estimation.
method Novel finite-sample performance guarantees for differentially private multivariate depth-based medians.
result Sharp performance guarantees for multivariate depth-based medians under differential privacy.
Paper connects GANs to robust estimation, leading to efficient computation of optimal estimators.
problem Statistical robust estimation under contaminated data models.
method Establishes connection between -GANs and depth functions through -Learning.
result Appropriate discriminator network structures in GANs lead to optimal robust estimators.
Geometric approach solves maximum likelihood for Cauchy-like distributions.
problem Estimating center and scatter robustly from heavy-tailed data.
method Geodesic convexity and symmetry spaces of noncompact type.
result Efficient numerical solution for robust estimates of location and spread.
New method makes neural networks more resilient to location-optimized adversarial patches.
problem Neural networks' vulnerability to adversarial patches that are visible but still effective.
method Developed a practical approach to optimize patch locations and applied adversarial training.
result Significantly improved robustness against adversarial patches on CIFAR10 and GTSRB.
Least Squares EM converges globally for log-concave mixtures.
problem Location estimation in mixtures of two log-concave densities.
method Least Squares EM algorithm applied to log-concave mixtures.
result Least Squares EM converges globally to the true location parameter.
New algorithms improve robust estimation in contaminated Gaussian models.
problem Simultaneous estimation of location and variance matrix in contaminated Gaussian models.
method Tractable adversarial algorithms with spline discriminators for robust estimation.
result Achieve minimax optimal rates or near-optimal rates under Huber's contamination model.
Study on conditions for achieving optimal robustness in statistical estimators.
problem Achieving the optimal robustness of estimators in statistical models.
method Developed a Wasserstein analogue of the Cramer-Rao inequality and investigated conditions for achieving the Wasserstein-Cramer-Rao lower bound.
result Conditions for the existence of asymptotically efficient estimators in one-parameter models and location-scale families.
GCN improves fault location in power grids with high accuracy.
problem Fault location in power distribution networks.
method Graph Convolutional Network (GCN) integrating multiple measurements and topology.
result GCN significantly outperforms other machine learning schemes in fault location accuracy.
This paper proposes BAT to balance accuracy and robustness in adversarial training.
problem Balancing accuracy and robustness in adversarial training models.
method Blind adversarial training (BAT) uses a cutoff-scale strategy to adaptively estimate a nonuniform budget for AEs.
result BAT improves the overall robustness of adversarial training models.
SkewD robustly discovers causal relationships in skewed noise models.
problem Distinguishing cause from effect in skewed noise models.
method SkewD extends normal-distribution framework to skew-normal setting for reliable inference.
result SkewD remains robust under high skewness, improving reliability.
Paper proposes an algorithm for robust estimation using Huber's criterion.
problem Non-convexity and non-robustness of joint maximum likelihood estimation.
method Block-wise minimization majorization framework with data-adaptive step sizes.
result Improved convergence and robustness in sparse learning.
Bayesian neural networks improve likelihood-free inference efficiency.
problem Efficient parameter inference from simulation models with uncertainty.
method Bayesian neural networks for summary statistics, adaptive sampling.
result More robust and efficient posterior estimation.
A new algorithm reduces regret in cooperative multi-agent bandits with heavy-tailed data.
problem Cooperative multi-agent bandits with heavy-tailed data.
method MP-UCB algorithm incorporating robust estimation with message-passing protocol.
result Optimal regret bounds for MP-UCB in various settings.
New method for high-dimensional regression with unknown scale parameter.
problem High-dimensional linear regression with unknown scale parameter.
method Penalized Huber -estimator with adaptive Lepski's method.
result The method effectively calibrates scale in high-dimensional robust regression.
Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitti…
Proposes DR-ME test for interpretable distributional treatment effects.
problem Detects invisible differences in treatment effects on distributional outcomes.
method Semiparametrically efficient finite-location test using kernel witnesses and orthogonal features.
result DR-ME reveals causal-discrepancy coordinates and has noncentral chi-square local power.
Paper proposes a feature-wise change detection method for improving indoor positioning accuracy.
problem Improving the quality of reference fingerprint maps in indoor positioning systems.
method Inspired by RANSAC, the paper uses resampling of features to estimate intermediate locations and identifies candidate locations using MJI.
result The approach improves positioning accuracy by 20% and achieves 90% change detection accuracy.
Paper analyzes robustness of MDPDE under INH setups.
problem Global reliability and breakdown behavior of MDPDE under INH.
method Asymptotic breakdown point analysis of MDPDE.
result Derives a theoretical lower bound for the asymptotic breakdown point.
DeepPos uses deep learning to improve indoor location accuracy.
problem Indoor GPS accuracy issues with mobile devices.
method Supervised autoencoder network for modeling CSI-based environments.
result Robust and efficient indoor localization system developed.
This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing elements. We propose a randomized two-step inference framework, and establish sufficient conditions on the required sample complexities und…
New method for fast volatility estimation robust to change points.
problem Robust high-frequency volatility estimation with change points.
method ℓ1-regularized power variation estimators using LARS for sparse estimation and dynamic programming for change point refinement.
result Minimax rates achieved for volatility estimators, providing accurate and smooth forecasts.
Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learningstat.ML
Estimates intrinsic dimension of data sets robustly to noise.
problem Estimating intrinsic dimension of noisy data sets.
method Quantum Cognition Machine Learning for data representation and spectral gap detection.
result Robust estimation of intrinsic dimension in the presence of Gaussian noise.
Bayesian method finds robust optima in expensive black-box functions.
problem Optimizing expensive black-box functions with sensitivity to inputs.
method Bayesian optimisation using Gaussian process prior and evolutionary algorithm for sampling and evaluation.
result Locating a region of design space with relatively insensitive performance to inputs.
Paper develops robust methods for large-scale testing without tuning parameters.
problem Heavy-tailed data in high-dimensional settings.
method Revisits Hodges-Lehmann estimator for robust inference without tuning parameters.
result Develops confidence intervals and controls false discovery proportion.
We improve maximum likelihood for location estimation in finite samples.
problem Estimating a parameter from samples with unknown or varying distribution.
method Use smoothed Fisher information for finite sample size and varying distributions.
result Recover optimal estimation theory for finite and arbitrary .
Study compares geometric approaches for shape and deformation statistics.
problem Characterizing statistical models of shapes and deformations.
method Information geometry and Wasserstein geometry.
result Wasserstein estimator is robust against waveform perturbation.
Study examines robust regression in high dimensions with heavy-tailed data.
problem Analyzing robust regression in high-dimensional settings with heavy-tailed data.
method Sharp asymptotic characterisation of M-estimators and ridge regression in elliptical distributions.
result Ridge regression is optimal and universal for finite second moments but can decay faster without them.
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrixstat.ML
Paper analyzes VI for location-scale families, proving robustness guarantees for mean and correlation recovery.
problem Misspecification in VI for intractable target densities.
method Variational inference on location-scale families with symmetries.
result VI recovers mean and correlation matrix under specific symmetries.
Paper introduces a robust generative model using weighted conjugate feature duality.
problem Training generative models can be affected by contamination, leading to noisy data.
method Introduces weighted conjugate feature duality in the framework of Restricted Kernel Machines (RKMs) to fine-tune the latent space.
result The weighted RKM is capable of generating clean images when training data is contaminated.
New risk class defined based on loss location and deviation.
problem Risk assessment in loss distributions.
method Wrapper around smooth loss functions, M-estimators, stochastic gradient methods.
result Finite-sample stationarity guarantees for stochastic gradient methods.
New private algorithms estimate location parameters with sub-Gaussian deviations.
problem Estimating location parameters with differential privacy and sub-Gaussian deviations for heavy-tailed data.
method Design two private algorithms for estimating the median and mean under differential privacy, showing sub-Gaussian deviations for unbounded random variables.
result Private median and mean estimators achieve sub-Gaussian deviations, unlike non-private counterparts which can have strictly worse deviations.
Paper proposes a deep autoencoder model to detect anomalies in CAV locations.
problem Early detection of anomalies in self-reported vehicle locations for CAVs.
method Unsupervised learning model based on deep autoencoder using vehicle locations and RSSI.
result The proposed model is effective and robust in detecting self-reported location anomalies.
GraphReach improves GNN performance by incorporating node positions.
problem Existing GNNs fail to capture node positions, leading to inaccurate predictions.
method GraphReach uses reachability estimations from anchor nodes to capture global node positions.
result GraphReach achieves up to 40% relative improvement in accuracy compared to state-of-the-art GNNs.
Bayesian approach improves performance in Gaussian process models.
problem Scalable posterior estimation in Gaussian process models.
method Revisiting variational inference techniques with Bayesian treatment of inducing variables and hyper-parameters.
result State-of-the-art performance demonstrated across various regression and classification problems.
An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimationcs.CV
A key step to driver safety is to observe the driver's activities with the face being a key step in this process to extracting information such as head pose, blink rate, yawns, talking to passenger which can then help derive higher level information such as distraction, drowsiness, intent, and where they are looking. I…
Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse modelsstat.ML
This study analyzes LTS in sparse models with finite sample error bounds.
problem Robust regression in high-dimensional sparse models with limited data.
method Non-asymptotic analysis of LTS error bounds.
result Established finite sample error bounds for LTS in sparse models.
Hölder-DPO aligns models robustly with noisy human feedback.
problem No existing alignment methods can handle severe label noise.
method Proposes Hölder-DPO, a principled alignment loss with provable redescending property.
result Hölder-DPO enables scalable human feedback valuation and improves model alignment.
Study evaluates methods for improving model robustness to various real-world distribution shifts.
problem Improving model robustness to real-world distribution shifts like geographic changes.
method Introduced new datasets and evaluated existing methods on four types of shifts (style, blurriness, location, camera operation).
result Data augmentations and larger models can improve robustness on real-world distribution shifts, contrary to prior claims.
Locates entanglement in curves using knot intensity distribution.
problem Finding robust methods for locating entanglement in embedded curves.
method Introducing knot intensity distribution as a local quantifier for entanglement contribution.
result Intensity distributions identify regions in knots accommodating topological changes.
Improved location estimation for high-dimensional data with finite sample size.
problem Estimating the shift in high-dimensional data with limited samples.
method Smoothed estimators and bounds on subgamma vectors.
result Convergence to Cramér-Rao bound for finite sample sizes.