Estimates linear model from noisy covariates and instruments using spectral regularization.
problem Estimating a linear model from many noisy covariates and instruments.
method Two-stage least squares with spectral regularization of canonical correlations.
result Upper and lower bounds on estimation error, proving optimality of the method with noisy data.
Matrix factorization reduces bias in causal inference with noisy covariates.
problem Bias in causal inference due to noisy and missing covariates.
method Matrix factorization to infer confounders from noisy covariates.
result Consistent estimation of average treatment effects in a linear regression setting.
Flexible estimator synthesizes noisy experiments and covariates for optimal effect estimation.
problem Simultaneous analysis of many noisy experiments with rich covariate information.
method Plug-in empirical Bayes estimator that synthesizes noisy experimental results and covariates.
result Within a constant factor of minimax for a simple data-generating model, and robust convergence guarantees hold under generality.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
Robust PCA method works under uncertain covariance.
problem Principal component analysis under uncertain covariance.
method Robust streaming PCA with temporal uncertainty set.
result Noisy power method is rate-optimal in our setting.
The study examines fairness metrics in noisy covariate settings, providing theoretical guarantees.
problem Fairness measurement under noisy covariate information.
method Theoretical analysis of group fairness metrics using proxies for covariates.
result Characterization of weaker conditions for accurate fairness evaluation.
New findings show noisy gradient descent can generalize well, even with non-SGD noise.
problem The role of noise in gradient descent's generalization ability.
method Analyzed the structure of SGD noise and proposed a new noisy gradient descent algorithm.
result Noises in classes different from SGD can also effectively regularize gradient descent.
Noisy natural gradient improves variational inference for Bayesian neural nets.
problem Tradeoff between simple and complex variational families in Bayesian neural nets.
method Adaptive weight noise in natural gradient ascent to implicitly fit variational posteriors.
result Noisy natural gradient algorithms can train full-covariance variational posteriors efficiently.
Estimating tree structured Gaussian Graphical Model from noisy data.
problem Recover the original independence structure from noisy observations.
method Address the unidentifiability of tree structured graphical models and provide an algorithm to find the equivalence class of trees.
result An O(n^3) algorithm to find the equivalence class of trees.
New methods rank players using covariates and comparisons, outperforming existing algorithms.
problem Ranking players based on incomplete and noisy pairwise comparisons.
method Three spectral ranking methods incorporating player covariates.
result Proposed methods outperform existing algorithms in simulations.
Paper develops a robust Bayesian optimization method for noisy zeroth-order settings.
problem Achieving robustness to distributional shift in machine learning.
method Distributionally robust Bayesian optimization (DRBO) algorithm for noisy zeroth-order optimization.
result DRBO algorithm provably obtains sub-linear robust regret in various settings.
Robustly estimates linear regression coefficients with adversarial and noisy data.
problem Estimating robust linear regression coefficients with adversarial and noisy data.
method Adversarial robust weighted Huber regression with polynomial computational complexity.
result Derives an estimation error bound that depends on the stable rank and condition number of the covariance matrix.
Deep neural networks can generalize well even with perfect fits to noisy data.
problem Understanding the conditions under which deep neural networks generalize well in the presence of noise.
method Comprehensive study of linear maximum margin classifiers, focusing on noisy and noiseless cases.
result Discovery of a phase transition in test error bounds for the noisy model.
Hybrid ResNet and RMT improve covariance matrix estimation for cryptocurrency portfolios.
problem Noisy, non-Gaussian financial data leads to unstable covariance matrices.
method Combines RMT regularization and ResNet learning for data-driven corrections.
result Hybrid estimator outperforms traditional methods in portfolio optimization.
The paper improves matrix completion with auxiliary covariates using LS estimation.
problem Matrix completion with noisy data and auxiliary covariates.
method Iterative least squares estimation with statistical properties derived.
result Asymptotic normal distributions of estimators for low-rank matrix and coefficient matrix.
GATs improve node regression on noisy graphs with provable advantage.
problem Improving node regression on graphs with noisy covariates and edges.
method Proposes a GAT designed for denoising proxy features in node regression.
result GAT achieves lower error in estimating regression coefficient and predicting responses.
Fast algorithm recovers principal eigenvector from noisy matrices.
problem Recovering the first principal eigenvector from noisy positive semidefinite matrices.
method Cone projected power iteration algorithm.
result Achieves polynomial time complexity and small error for certain convex cones.
Recursive KalmanNet generalizes well in noisy, out-of-distribution scenarios.
problem Generalization in noisy, out-of-distribution scenarios.
method Recurrent neural network guided by a Kalman filter.
result Recursive KalmanNet performs well in scenarios with different temporal dynamics from training data.
A new model for analyzing noisy, asynchronous high-frequency data.
problem Challenges in analyzing intraday correlations due to market microstructure noise and asynchronicity.
method Score-driven conditional correlation model using multivariate local-level model with score-driven covariance matrices.
result Market microstructure noise is effectively accounted for, leading to more accurate correlation estimates.
Proposes DGCN with trajectory sampling for data-efficient policy search in MBRL.
problem Improving data efficiency in model-based reinforcement learning.
method Combines trajectory sampling and DGCN for uncertainty propagation in probabilistic world models.
result Improves sample-efficiency over other uncertainty propagation methods and probabilistic models.
Graphical Lasso detects anomalies in noisy data by splitting covariance matrix into clean and outlier parts.
problem Detecting anomalies in large, noisy data sets.
method Robust Graphical Lasso (Rglasso) using ADMM optimization.
result Rglasso outperforms standard robust methods in accuracy and speed.
We introduce a stochastic model for noisy vector fields on manifolds.
problem Noisy vector fields violate the assumption of parallel transport in stochastic analysis.
method We define a stochastic Lie bracket that induces torsion and analyze its consequences.
result The stochastic Lie bracket induces torsion in expectation.
The salient properties of large empirical covariance and correlation matrices are studied for three datasets of size 54, 55 and 330. The covariance is defined as a simple cross product of the returns, with weights that decay logarithmically slowly. The key general properties of the covariance matrices are the following…
Improves classification accuracy with noisy labels using generative classifiers.
problem Handling noisy labels in large-scale datasets.
method Robust Generative Classifier (RoG) on top of pre-trained DNNs.
result Significantly improves classification accuracy with no re-training of the deep model.
Principal Component Analysis (PCA) is the most common nonparametric method for estimating the volatility structure of Gaussian interest rate models. One major difficulty in the estimation of these models is the fact that forward rate curves are not directly observable from the market so that non-trivial observational e…
Structured credal learning separates covariate shift and label disagreement.
problem Uncertainty in real-world learning tasks due to covariate shift and noisy labels.
method Introduces a structured credal learning framework that explicitly separates these sources.
result Geometric bounds and decomposition reveal how covariate shifts affect label disagreement contributions.
New clustering algorithm handles noisy data efficiently.
problem EM algorithm's limitations in noisy, high-dimensional data.
method Flexible EM-like algorithm with semi-parametric scale estimation.
result Outperforms other clustering methods on real data.
Improved stability for large-scale Bayesian sampling.
problem Reducing instability in Langevin dynamics for large datasets.
method Introducing a modified CCAdL thermostat with a scaling and squaring method and a truncated Taylor series approximation.
result Significantly improved numerical stability and accuracy over existing methods.
New method for factor analysis using nuclear and ℓ0 norms.
problem Finding a low-rank plus sparse decomposition from noisy covariance matrix.
method Formulated an optimization problem with nuclear norm, ℓ0 norm, and KL divergence. Used alternating minimization algorithm. result Algorithm effectively decomposes covariance matrices in synthetic and real datasets.
A corrected EI acquisition function handles noisy observations in Bayesian optimization.
problem Noisy observations in Bayesian optimization.
method Proposes a modified expected improvement (EI) acquisition function that incorporates covariance information from the Gaussian Process model.
result Achieves a sublinear convergence rate on cumulative regret bound under heteroscedastic observation noise.
Study of accelerated dynamics for convex function minimization with noisy gradients.
problem Minimizing smooth convex functions with noisy gradients.
method Formulate and study continuous-time stochastic dynamics, prove convergence rates.
result Derive estimates of convergence rates for function values, both persistent and asymptotic.
In this paper, we obtain a property of the expectation of the inverse of compound Wishart matrices which results from their orthogonal invariance. Using this property as well as results from random matrix theory (RMT), we derive the asymptotic effect of the noise induced by estimating the covariance matrix on computing…
Review of tools from RMT for estimating large covariance matrices.
problem Estimating large covariance matrices from noisy data.
method Random Matrix Theory (RMT) methods and analytical techniques.
result Rotationally Invariant Estimators (RIE) are superior to existing methods.
A new model uses firm characteristics to predict asset covariances.
problem Risk models are noisy and dependent on historical returns.
method Characteristic-Driven Dynamic Factor Model (CD-DFM) that learns latent representations from firm characteristics.
result CD-DFM produces interpretable factor portfolios and competitive covariance forecasts.
This paper improves volatility estimation for noisy multivariate data.
problem Nonparametric inference for nonlinear volatility functionals of multivariate Itô semimartingales.
method Pre-averaging and truncation techniques to handle noise and jumps; second-order expansion for bias correction; stable central limit theorems for asymptotic results.
result Achieves optimal convergence rate and stable central limit theorems with estimable asymptotic covariance matrices.
We apply variational inference to learn vehicle trajectory parameters from noisy data.
problem Learning parameters for vehicle trajectory estimation from noisy measurements.
method Gaussian variational inference with parameter learning in a motion and sensor model context.
result High-quality state estimates achieved even with outliers and false loop closures.
New linear denoiser outperforms standard Wiener filter in noisy data.
problem Improving denoising performance for unknown covariance data.
method Synthetically constructed noisy samples to train a linear denoiser using least-squares approximation.
result Optimal denoiser found using the Convex Gaussian Min-Max Theorem (CGMT) for proportional regime.
Improved covariate shift handling with node-based Bayesian neural networks.
problem Improving generalization under covariate shift in neural networks.
method Introduced node-based Bayesian neural networks that learn latent noise variables to represent input corruptions.
result Node-based BNNs perform well under covariate shift due to input perturbations, improving uncertainty estimation and robustness.
Enhances UPSA to reduce noise in financial data.
problem Noise in financial data affects UPSA's performance.
method Time-averaging optimal penalty weights and using Average Oracle correlation eigenvalues.
result Combining time-averaging and Average Oracle correlation eigenvalues improves UPSA's performance.
New bounds improve minimax estimation of banded precision matrices.
problem Estimating banded precision matrices with optimal rates.
method Inverting wider blocks of empirical covariance matrices to estimate subblocks of precision matrices.
result Minimax rate matches for banded covariance matrices, improving previous bounds.
PCR robust to noisy, missing, and mixed-valued covariates.
problem Handling noisy, missing, and mixed-valued covariates in PCR.
method PCR is equivalent to HSVT pre-processing; establishes robustness and finite-sample analysis.
result PCR robust to noise, equivalent to RSC, and can learn good predictive models.
Robust Conformalized Selection controls FDR under noisy responses.
problem Existing conformal selection methods fail to control FDR under contaminated calibration data.
method RCS framework for selective classification with valid FDR control under label contamination.
result RCS framework controls FDR and maintains power under contaminated calibration data.
Forecast reconciliation improves portfolio risk forecasts, especially when true covariance is known.
problem Improving portfolio risk forecasts using multivariate GARCH models.
method Combining univariate and multivariate forecasts with forecast reconciliation techniques.
result Forecast reconciliation improves over standard multivariate approaches, especially when true covariance is known.
Method selects significant spatial covariates in noisy data.
problem Identifying true spatial covariates in noisy data.
method Combines sparsity-promoting estimation with noise-robust model selection.
result Method reliably recovers true covariates under diverse noise scenarios.
RFMs transition from linear to nonlinear under specific input-label correlation.
problem Understanding the transition from linear to nonlinear behavior in RFMs.
method Analyzing RFMs under spiked covariance designs, characterizing the interaction between anisotropy and input-label correlation.
result The RFM generalization error is governed by the strength of input-label correlation, leading to a clear nonlinear advantage above a specific boundary.
Paper improves prediction accuracy in sparse linear models with missing data.
problem Enhancing prediction accuracy in sparse linear models with missing information.
method Introduces an approach combining sparse regression and covariance matrix estimation.
result Improves matrix completion accuracy and feature selection precision, reducing prediction MSE.
MPVAE learns latent embeddings and label correlations for multi-label classification.
problem Challenging task of predicting multiple targets with label correlations.
method Proposes MPVAE, a novel framework that learns latent embedding spaces and label correlations using a Multivariate Probit model.
result MPVAE outperforms state-of-the-art methods on various application domains and is robust under noisy settings.
Study nonparametric covariance function estimation for noisy data.
problem Estimating covariance function from discrete noisy data in high dimensions.
method Adaptive learning-based estimators, including deep learning.
result Established oracle inequality and convergence rates for deep learning estimators.