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.
FF algorithm uses goodness as a likelihood-ratio test for scalar normalization.
Enhances forecasting of complex systems using FKMD.
FF algorithm uses goodness as a measure of input quality, derived from likelihood-ratio tests.
This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations that generalize the usu…
In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF). In this paper we add additional information to Gaussian SVM by considerin…
Flexible classifier using Mahalanobis distances for non-elliptical distributions.
Extends Mahalanobis distance to Banach spaces for anomaly detection.
A method uses Shapley values and Mahalanobis distances to explain multivariate outliers.
A fundamental question in data analysis, machine learning and signal processing is how to compare between data points. The choice of the distance metric is specifically challenging for high-dimensional data sets, where the problem of meaningfulness is more prominent (e.g. the Euclidean distance between images). In this…
The paper connects neural networks to Mahalanobis distance for interpretability.
Compressing data helps learn Mahalanobis metrics effectively.
The Mahalanobis distance-based confidence score, a recently proposed anomaly detection method for pre-trained neural classifiers, achieves state-of-the-art performance on both out-of-distribution (OoD) and adversarial examples detection. This work analyzes why this method exhibits such strong performance in practical s…
Learning Mahalanobis metric spaces is an important problem that has found numerous applications. Several algorithms have been designed for this problem, including Information Theoretic Metric Learning (ITML) [Davis et al. 2007] and Large Margin Nearest Neighbor (LMNN) classification [Weinberger and Saul 2009]. We study…
Connects robust optimization to conformal prediction for uncertainty sets.
We propose a low-rank approach to learning a Mahalanobis metric from data. Inspired by the recent geometric mean metric learning (GMML) algorithm, we propose a low-rank variant of the algorithm. This allows to jointly learn a low-dimensional subspace where the data reside and the Mahalanobis metric that appropriately f…
Bayesian nonparametric models improve OOD detection, especially with complex covariance structures.
For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, a…
This paper learns user preferences from comparisons using Mahalanobis metrics.
Proposes ridge regression on Riemannian manifolds for time-series prediction.
Study robustness of early-stopping GD for linear regression attacks.
This paper quantifies how hard it is to identify specific data points in machine learning models.
Boosts kernel two-sample test power with multiple kernels.
In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semi-definiteness) for use in a local …
Develops a computationally tractable differentially private mean estimator called the balloon mean.
2L-FUSE enhances feature sparsity through kernel learning.
Proposes a text perturbation method using a Mahalanobis metric to balance privacy and utility.
We derive a new radial link for binary classification under shared elliptical distributions.
A new algorithm estimates mean adaptively to covariance, faster and more flexible than existing methods.
A new test validates ensemble models against the null hypothesis.
Develops a method for stress testing correlations of financial portfolios.
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing t…
Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically…
A new algorithm reduces online exp-concave optimization runtime.
Kernel functions in support vector machines (SVM) are needed to assess the similarity of input samples in order to classify these samples, for instance. Besides standard kernels such as Gaussian (i.e., radial basis function, RBF) or polynomial kernels, there are also specific kernels tailored to consider structure in t…
The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning…
Paper proposes a robust metric learning algorithm.
The distance metric plays an important role in nearest neighbor (NN) classification. Usually the Euclidean distance metric is assumed or a Mahalanobis distance metric is optimized to improve the NN performance. In this paper, we study the problem of embedding arbitrary metric spaces into a Euclidean space with the goal…
The paper tackles learning smooth distance functions using query-based methods.
Recent work in metric learning has significantly improved the state-of-the-art in k-nearest neighbor classification. Support vector machines (SVM), particularly with RBF kernels, are amongst the most popular classification algorithms that uses distance metrics to compare examples. This paper provides an empirical analy…
The paper evaluates and improves uncertainty estimates in neural networks for safety-critical applications.
Robust multivariate linear regression methods for online and offline use.
Extends metrics for SPD matrices to infinite dimensions.
The crucial importance of metrics in machine learning algorithms has led to an increasing interest in optimizing distance and similarity functions, an area of research known as metric learning. When data consist of feature vectors, a large body of work has focused on learning a Mahalanobis distance. Less work has been …
Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks with the softmax classifier are known to produce highly overconfid…
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems. A number of recent papers have proposed methods for detecting anomalous image data that appear different from known inlier data samples, including reco…
Bayesian method selects subsets for LMMs with structured dependence.
Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed b…
Elliptical Attention improves transformer performance by focusing on contextually relevant features.