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.
Unified framework for shrinkage, thresholding, and regularization in normal mean estimation and linear regression.
A new method estimates marginal likelihood using normalizing flows.
A method for converting NIW parameters for better estimation.
We consider graphs Sigma^n in R^m with prescribed mean curvature and flat normal bundle. Using techniques of Schoen, Simon and Yau, and Ecker-Huisken, we derive an interior curvature estimate of the form |A|^2<=C/R^2 up to dimension n<=5, where C is a constant depending on natural geometric data of Sigma^n only. This g…
In this note, we derive an approximation for the mean curvature normal vector on vertices of triangulated surface meshes from the Young-Laplace equation and the force balance principle. We then demonstrate that the approximation expression from our physics-based derivation is equivalent to the discrete Laplace-Beltrami…
Stein shrinkage improves BN robustness against adversarial attacks.
We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a -variate normal means a samples is only re…
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Mean…
We solve the mean parametrization of von Mises-Fisher distribution.
Proposes a new framework for deep learning conditional mean estimation with confidence regions.
Study improves BN TTA under distribution shift using higher-order asymptotics.
AQFC method estimates mesh curvatures using quadratic surfaces.
While the authors of Batch Normalization (BN) identify and address an important problem involved in training deep networks-- Internal Covariate Shift-- the current solution has certain drawbacks. Specifically, BN depends on batch statistics for layerwise input normalization during training which makes the estimates of …
Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch. One problem with this approach is that the anomaly score of rare-normal sounds becomes hig…
New method constructs synthetic treatment groups without mean exchangeability assumption.
Stochastic Volatility in Mean models with heavy-tailed distributions using Hidden Markov Models
Paper improves statistical efficiency of median-of-means estimator for Byzantine robust distributed inference.
While the authors of Batch Normalization (BN) identify and address an important problem involved in training deep networks-- \textit{Internal Covariate Shift}-- the current solution has certain drawbacks. For instance, BN depends on batch statistics for layerwise input normalization during training which makes the esti…
The paper uses deep neural networks to estimate and infer ATE without needing to know the dimension of the data.
New algorithm improves learning in noisy networks with robust performance.
Proposes a new method to estimate Bayesian neural network depth.
CPME embeds counterfactual outcomes in RKHS for flexible policy evaluation.
We provide a unified treatment of a broad class of noisy structure recovery problems, known as structured normal means problems. In this setting, the goal is to identify, from a finite collection of Gaussian distributions with different means, the distribution that produced some observed data. Recent work has studied s…
Enhances normal mean estimation with side info using NIT approach.
Although consistency is a minimum requirement of any estimator, little is known about consistency of the mean partition approach in consensus clustering. This contribution studies the asymptotic behavior of mean partitions. We show that under normal assumptions, the mean partition approach is consistent and asymptotic …
Paper shows robust estimators converge to true risk minimizers at optimal rates.
Robust GQDA improves classification accuracy in non-Normal data.
In this overview report we generalize Erhard Heinz' curvature estimate for minimal graphs in R^3 to graphs in R^n of prescribed mean curvature. Secondly, we analyse these problems in the frame of the outer differential geometry which leads us to the notions of normal torsion and normal curvature for immersions in R^4.
This paper presents a class of new algorithms for distributed statistical estimation that exploit divide-and-conquer approach. We show that one of the key benefits of the divide-and-conquer strategy is robustness, an important characteristic for large distributed systems. We establish connections between performance of…
New analysis of annealing paths in sampling and estimation.
The paper analyzes the risk of CV-tuned regularized estimators and connects it to SURE.
We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification. In theory, we build a bridge to connect the estimation error of the mean difference and the misclassificat…
The study compares parametric and nonparametric models for estimating mean-variance mixtures and finds that nonparametric models perform better.
New algorithms reduce communication for sparse mean estimation in noisy distributed systems.
New research shows shrinkage methods re-scale portfolio efficient frontiers under distributional misspecification.
The paper analyzes -means clustering for missing data, proving statistical guarantees under MCAR.
Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mea…
Utilizing a weight matrix we study surfaces of prescribed weighted mean curvature which yield a natural generalisation to critical points of anisotropic surface energies. We first derive a differential equation for the normal of immersions with prescribed weighted mean curvature, generalising a result of Clarenz and vo…
New insights into empirical Bayes and compound decision problems with improved regret bounds.
The minimum message length principle is an information theoretic criterion that links data compression with statistical inference. This paper studies the strict minimum message length (SMML) estimator for -dimensional exponential families with continuous sufficient statistics, for all . The partition of an …
Proves a special type of submanifolds in a curved space.
Improved estimation of higher order integrals using shrinkage techniques.
New framework reduces private mean estimation error with optimal efficiency.
We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator -- that is, a measurable function of the observation…
This paper provides a framework to analyze stochastic gradient algorithms in a mean squared error (MSE) sense using the asymptotic normality result of the stochastic gradient descent (SGD) iterates. We perform this analysis by taking the asymptotic normality result and applying it to the finite iteration case. Specific…
Convexity preserved in curved surfaces moving at concave speeds.
We prove mean curvature and volume comparison estimates on smooth metric measure spaces when their integral Bakry-Émery Ricci tensor bounds, extending Wei-Wylie's comparison results to the integral case. We also apply comparison results to get diameter estimates, eigenvalue estimates and volume growth estimates on smoo…
Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence because of its capability in handling re-centering and re-scaling of both inputs and weight matrix. However, the computational overhead introduced by LayerNorm makes these…