arXiv research

A new algorithm selects data subsets avoiding outliers and high leverage points.

Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data poi…

New robust estimator for high-dimensional data with outliers and leverage points.

D2D converts CLDs into SDMs to explore leverage points under uncertainty.

Estimates volatility of volatility and leverage effect using high-frequency options data.

Effective risk control must make a tradeoff between the microprudential risk of exogenous shocks to individual institutions and the macroprudential risks caused by their systemic interactions. We investigate a simple dynamical model for understanding this tradeoff, consisting of a bank with a leverage target and an unl…

PERCEPT detects changes in high-dimensional data streams using topological data analysis.

t-SNE algorithm's points remain bounded under gradient flow.

New method estimates robust mean in high dimensions with minimized outliers.

Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information. However, previous results require $Ω(d)$ samples to approximate Hessians, where $d$ is the dimension of data points, making it less practica…

This paper describes a novel approach to change-point detection when the observed high-dimensional data may have missing elements. The performance of classical methods for change-point detection typically scales poorly with the dimensionality of the data, so that a large number of observations are collected after the t…

Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.

Improved GoF statistics using entropy-regularized optimal transport for multivariate rank.

We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high…

This paper models how features influence event triggers in high-dimensional networks.

GTBO uses group testing to optimize high-dimensional functions efficiently.

Gradient descent solves robust mean estimation in high dimensions.

Study proposes a neural network approach for high inflation investment portfolios with leverage constraints.

The paper analyzes LASSO penalization for high-dimensional Beta regression models.

ODBAE detects complex phenotypes in biological data.

This letter presents a new spectral-clustering-based approach to the subspace clustering problem. Underpinning the proposed method is a convex program for optimal direction search, which for each data point d finds an optimal direction in the span of the data that has minimum projection on the other data points and non…

New insights into ridge regression with correlated data, improving risk prediction.

Scalable solver reduces PDE uncertainty with active learning.

G-Net constructs binary neural networks with high accuracy using randomized binary embeddings.

Recent years have seen increased interest in performance guarantees of gradient descent algorithms for non-convex optimization. A number of works have uncovered that gradient noise plays a critical role in the ability of gradient descent recursions to efficiently escape saddle-points and reach second-order stationary p…

Paper proposes semi-supervised learning using change points for sequence classification.

In this paper, we study stochastic non-convex optimization with non-convex random functions. Recent studies on non-convex optimization revolve around establishing second-order convergence, i.e., converging to a nearly second-order optimal stationary points. However, existing results on stochastic non-convex optimizatio…

We show that typical behaviors of market participants at the high frequency scale generate leverage effect and rough volatility. To do so, we build a simple microscopic model for the price of an asset based on Hawkes processes. We encode in this model some of the main features of market microstructure in the context of…

New method speeds up k-means clustering for large k by improving nearest-neighbor search.

We use a simple agent based model of value investors in financial markets to test three credit regulation policies. The first is the unregulated case, which only imposes limits on maximum leverage. The second is Basle II and the third is a hypothetical alternative in which banks perfectly hedge all of their leverage-in…

Unified framework denoises data and abstains from uncertain predictions.

Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the …

CobBO optimizes expensive functions in high dimensions by using a two-stage kernel approach.

The stochastic leverage effect, defined as the standardized covariation between the returns and their related volatility, is analyzed in a stochastic volatility model set-up. A novel estimator of the effect is defined using a pre-estimation of the Fourier coefficients of the return and the volatility processes. The con…

New method detects if data points were used in training models with low cost and high power.

Gradient flow of ReLU networks converges in low-correlation high-dimensional data.

Thanks to the access to labeled orders on the Cac40 index future provided by Euronext, we are able to quantify market participants contributions to the volatility in the diffusive limit. To achieve this result we leverage the branching properties of Hawkes point processes. We find that fast intermediaries (e.g., market…

A new method for selecting high quality itemsets from a large collection.

We propose a new class of data-independent locality-sensitive hashing (LSH) algorithms based on the fruit fly olfactory circuit. The fundamental difference of this approach is that, instead of assigning hashes as dense points in a low dimensional space, hashes are assigned in a high dimensional space, which enhances th…

New test detects local changes in high-dimensional Gaussian graphical models online.

New method learns dynamical systems efficiently using active learning.

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learnin…

Efficiently estimates common mean in high-dimensional data with noisy subset.

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In …

A registration-free framework monitors shape and color in 4D point clouds.

Paper tackles leverage effect estimation from noisy data.

Paper uses TDA to assess cryptocurrency risk by measuring phase space instability.

A new method optimizes slicing directions for SW distances to improve high-dimensional probability measure comparison.