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.
New method stabilizes private LASSO for high-dimensional data with diverse covariate scales.
The paper assesses machine learning robustness with covariate perturbations.
A method to explain disease transformation using biomarker covariance matrices.
This paper examines how adversarial perturbations affect model performance and equilibrium learning.
New method uses unlabeled data to improve model robustness across different environments.
Enhances GBDT robustness with one-hot encoding and regularization.
We propose a new input perturbation mechanism for publishing a covariance matrix to achieve -differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, We apply this mechanism to principal component analysis. Our mechanism is able to keep the positive semi-definitene…
Improved covariate shift handling with node-based Bayesian neural networks.
Study robust estimation of principal components under adversarial perturbations.
Robust statistics traditionally focuses on outliers, or perturbations in total variation distance. However, a dataset could be corrupted in many other ways, such as systematic measurement errors and missing covariates. We generalize the robust statistics approach to consider perturbations under any Wasserstein distance…
Short proof shows how ridge regression works with random data.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
Proves stability of Minkowski space-time for Einstein-Yang-Mills equations.
PACE-GGM uses Gaussian mechanism for private covariance estimation.
Study projective representations of infinite-dimensional Hilbert-Lie groups.
This the first in a series of papers whose ultimate goal is to establish the full nonlinear stability of the Kerr family for . The paper builds on the strategy laid out in \cite{KS} in the context of the nonlinear stability of Schwarzschild for axially symmetric polarized perturbations. In fact the central id…
A new GNN architecture called coVariance neural network (VNN) improves stability and transferability of covariance matrix analysis.
Differentially private method for estimating individualized treatment rules.
Capsule Networks attempt to represent patterns in images in a way that preserves hierarchical spatial relationships. Additionally, research has demonstrated that these techniques may be robust against adversarial perturbations. We present an improvement to training capsule networks with added robustness via non-paramet…
New method defines GCM spheres in Kerr perturbations, proving their stability.
Noise in SGD affects overparameterized models, favoring sparse solutions.
In this paper we study the behaviour of the continuous spectrum of the Laplacian on a complete Riemannian manifold of bounded curvature under perturbations of the metric. The perturbations that we consider are such that its covariant derivatives up to some order decay with some rate in the geodesic distance from a fixe…
We study the accuracy of estimating the covariance and the precision matrix of a -variate sub-Gaussian distribution along a prescribed subspace or direction using the finite sample covariance. Our results show that the estimation accuracy depends almost exclusively on the components of the distribution that correspo…
We consider problems in which a system receives external \emph{perturbations} from time to time. For instance, the system can be a train network in which particular lines are repeatedly disrupted without warning, having an effect on passenger behavior. The goal is to predict changes in the behavior of the system at par…
We consider streaming principal component analysis when the stochastic data-generating model is subject to perturbations. While existing models assume a fixed covariance, we adopt a robust perspective where the covariance matrix belongs to a temporal uncertainty set. Under this setting, we provide fundamental limits on…
Mean Field Variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is its (sometimes severe) underestimates of the uncertainty of model variables and lack of information about model variable covariance.…
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation of common sources of variability (electronic, electrical, biological) by constr…
We study the small perturbations of the -dimensional Milne model for the Einstein-Klein-Gordon (EKG) system. We prove the nonlinear future stability, and show that the perturbed spacetimes are future causally geodesically complete. For the proof, we work within the constant mean curvature (CMC) gauge and focus on …
Variational inference has become one of the most widely used methods in latent variable modeling. In its basic form, variational inference employs a fully factorized variational distribution and minimizes its KL divergence to the posterior. As the minimization can only be carried out approximately, this approximation i…
A general framework for principal component analysis (PCA) in the presence of heteroskedastic noise is introduced. We propose an algorithm called HeteroPCA, which involves iteratively imputing the diagonal entries of the sample covariance matrix to remove estimation bias due to heteroskedasticity. This procedure is com…
This paper constructs GCM hypersurfaces in Kerr spacetimes.
The perturbative approach to nonlinear Sigma models and the associated renormalization group flow are discussed within the framework of Euclidean algebraic quantum field theory and of the principle of general local covariance. In particular we show in an Euclidean setting how to define Wick ordered powers of the underl…
Paper analyzes ensemble Kalman updates for effective dimension and localization.
In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information. In the differential privacy framework, we develop a differentially private ridge estimator by perturbing the sample covariance matrix. Then we develop a differentially private graphical lasso estimator by…
Novel Bayesian neural network method for robustness.
Study robust covariance estimation in large data with concentrated vectors.
Adaptive classifier optimizes high-dimensional data with spiked covariance structure.
GATs improve node regression on noisy graphs with provable advantage.
Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change…
New method for robustly interpreting ML models using quantile constraints and Wasserstein projections.
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of MFVB is that it underestimates the uncertainty of model variables (sometimes severely) and provides no information about model variable cova…
Study on friction forces for nonholonomic systems using affine connections.
A brief review on the progress made in the study of Chern-Simons gauge theory since its relation to knot theory was discovered ten years ago is presented. Emphasis is made on the analysis of the perturbative study of the theory and its connection to the theory of Vassiliev invariants. It is described how the study of t…
New insights into spectral statistics of sample covariance matrix for stable linear systems.
Improved robustness for high-dimensional Kalman filtering.
Proposes a text perturbation method using a Mahalanobis metric to balance privacy and utility.
Dropout training is shown to be optimal for adversarial covariate corruption.
New bounds for private matrix approximation using Gaussian noise and Dyson Brownian Motion.