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.
Improved nonparametric regression with debiasing for root-n consistency.
New method estimates mutual information using normalizing flows.
We prove that the only compact, origin-symmetric, strictly convex ancient solutions of the planar centro-affine normal flows are contracting origin-centered ellipses.
We consider partially observed multiscale diffusion models that are specified up to an unknown vector parameter. We establish for a very general class of test functions that the filter of the original model converges to a filter of reduced dimension. Then, this result is used to justify statistical estimation for the u…
New priors can update posteriors without re-estimating likelihoods.
New method efficiently interpolates nonparametric density estimators.
Study geodesic Lie groups' convergence to limits with quantitative estimates.
Improved MoM estimator enhances classical shadows protocol for quantum measurements.
CDRE estimates density ratios in streaming data without historical samples.
Improved KernelSHAP via linear regression for ML model interpretation.
New nonconvex penalty smooths at origin for deep learning.
Javanmard and Montanari propose a debiased estimator for high-dimensional regression.
We investigated the topological properties of stock networks through a comparison of the original stock network with the estimated stock network from the correlation matrix created by the random matrix theory (RMT). We used individual stocks traded on the market indices of Korea, Japan, Canada, the USA, Italy, and the …
Paper solves long-standing Gaussian curvature conjecture for minimal graphs.
Simplified argument for second order estimate in quaternionic Calabi-Yau problem.
Calibrated Prediction-Powered Inference improves semisupervised mean estimation by calibrating prediction scores.
δ-CLUE generates diverse explanations for model uncertainty.
The paper analyzes the statistical properties of GANs using -divergence.
Due to the limited resources and the scale of the graphs in modern datasets, we often get to observe a sampled subgraph of a larger original graph of interest, whether it is the worldwide web that has been crawled or social connections that have been surveyed. Inferring a global property of the original graph from such…
Replication study shows Deep-SE still not as effective as previously thought for agile effort estimation.
The paper proves the consistency and efficiency of a volatility estimator in noisy data.
Proves Hessian estimates for special Lagrangian equation with new proofs.
Post-estimation smoothing improves prediction accuracy with structural indices.
Paper presents a new way to estimate model changes without full model evaluation.
New method improves PCA for high-dimensional data with n < p.
We give a new and complete proof of Hamilton's injectivity radius estimate for sequences with bounded and almost nonnegative curvature operators, unbounded diameters, and bump-like origins. Such sequences arise in particular from dilations about a singularity of the Ricci flow on a 3-manifold.
Study on residual Monge-Ampère mass for symmetric plurisubharmonic functions.
Clinical models can be unstable, leading to unreliable predictions.
Study improves curvature estimate for stable marginally outer trapped hypersurfaces with a free boundary.
We extend the randomized singular value decomposition (SVD) algorithm \citep{Halko2011finding} to estimate the SVD of a shifted data matrix without explicitly constructing the matrix in the memory. With no loss in the accuracy of the original algorithm, the extended algorithm provides for a more efficient way of matrix…
This paper considers the problem of estimating a high-dimensional vector of parameters from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (…
In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to…
This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the dist…
Our objective is to estimate the unknown compositional input from its output response through an unknown system after estimating the inverse of the original system with a training set. The proposed methods using artificial neural networks (ANNs) can compete with the optimal bounds for linear systems, where convex optim…
New method reduces copyright risks in AI-generated images.
Paper constructs non-symmetric collapsing spacetimes without symmetries.
Kuwert and Schätzle showed in 2001 that the Willmore flow converges to a standard round sphere, if the initial energy is small. In this situation, we prove stability estimates for the barycenter and the quadratic moment of the surface. Moreover, in codimension one we obtain stability bounds for the enclosed volume and …
When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then using these ratios as rewards in a standard reinforcement learning (RL) algorithm. Traditionally, estimation of the distribution ratio requi…
Novel compression method preserves privacy while reducing communication costs.
This study proposes sparse estimation methods for the generalized linear models, which run one of least angle regression (LARS) and least absolute shrinkage and selection operator (LASSO) in the tangent space of the manifold of the statistical model. This study approximates the statistical model and subsequently uses e…
Proposes a method to stabilize treatment effect estimation with unbalanced data.
The Lugannani-Rice formula is a saddlepoint approximation method for estimating the tail probability distribution function, which was originally studied for the sum of independent identically distributed random variables. Because of its tractability, the formula is now widely used in practical financial engineering as …
Robustly aligns datasets with partial GW distance to handle contamination.
As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets …
Investigates numerical issues in GP interpolation parameter estimation.
Proposes a new method for kernel density estimation using stagewise minimization and a simple dictionary.
In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted …
We analyze differences between two information-theoretically motivated approaches to statistical inference and model selection: the Minimum Description Length (MDL) principle, and the Minimum Message Length (MML) principle. Based on this analysis, we present two revised versions of MML: a pointwise estimator which give…