arXiv research

Extends multidimensional scaling to analyze three-way asymmetric proximities.

Wide neural networks with asymmetrical node scaling converge globally and learn features.

New model captures asymmetric rough volatility with Zumbach effect.

This study examines asymmetric cross-correlations in cryptocurrency markets using fractal analysis.

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An…

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a …

ADGAN improves risk tolerance prediction by aligning cross-domain data.

New guarantees for asymmetric sketching in compressive learning.

TSVQR captures heterogeneous and asymmetric data using quantile regression.

We propose a new volatility model based on two stylized facts of the volatility in the stock market: clustering and leverage effect. We calibrate our model parameters, in the leading order, with 77 years Dow Jones Industrial Average data. We find in the short time scale (10 to 50 days) the future volatility is sensitiv…

The price impact for a single trade is estimated by the immediate response on an event time scale, i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of …

The {\em drawdown} process $Y$ of a completely asymmetric Lévy process $X$ is equal to $X$ reflected at its running supremum $\bar{X}$: $Y = \bar{X} - X$. In this paper we explicitly express in terms of the scale function and the Lévy measure of $X$ the law of the sextuple of the first-passage time of $Y$ over the leve…

This work models financial market returns with asymmetric Tsallis distributions, improving fit over symmetric q-Gaussians.

This paper analyzes ETFs with Taiwan exposure, finding heavy tails and asymmetric volatility.

DEUA detects diffusion-generated images by accounting for different types of uncertainty.

Gradient descent solves asymmetric low-rank matrix sensing without balancing.

A new UCB policy improves reward-cost ratio estimation in budgeted MAB.

Study of geometric analysis on asymmetric metric spaces, including heat flow and Sobolev spaces.

Quantum walk model captures asymmetry and bimodality in long-term financial returns.

New metrics for Anosov representations defined from Thurston's asymmetric metrics.

Generalizes Thurston's asymmetric metric to flat metrics.

Theoretical justification for asymmetric actor-critic algorithms in reinforcement learning.

This work presents deep asymmetric networks with a set of node-wise variant activation functions. The nodes' sensitivities are affected by activation function selections such that the nodes with smaller indices become increasingly more sensitive. As a result, features learned by the nodes are sorted by the node indices…

We provide a microfoundation for linear price impact models in a stationary market.

We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. Howev…

New asymmetric kernel methods improve feature learning.

The article confirms two quasi-alternating surgeries for 9 asymmetric L-space knots.

Develops a new method to model overlapping asymmetric datasets effectively.

Asymmetric expansion preserves convexity in hyperbolic geometry.

The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…

In this paper we show how the study of asymmetric R&D alliances, that are those between young and small firms and large and MNEs firms for knowledge exploration and/or exploitation, requires the adoption of a coopetitive framework which consider both collaboration and competition. We draw upon the literature on asymmet…

We present a large scale hyperbolic recommender system. We discuss why hyperbolic geometry is a more suitable underlying geometry for many recommendation systems and cover the fundamental milestones and insights that we have gained from its development. In doing so, we demonstrate the viability of hyperbolic geometry f…

In foreign exchange markets monotonic rate changes can be observed in time scale of order of an hour on the days that governmental interventions took place. We estimate the starting time of an intervention using this characteristic behavior of the exchange rates. We find that big amount of interventions can shift the a…

Bayesian VI copula models capture asymmetric intraday equity dependence.

This work describes compactifications of metric spaces and vector spaces using asymmetric norms.

We investigate the large-volatility dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after large volatilities is characterized by a power law, and the exponents $p_\pm$ usually vary with the strength of the large vo…

We investigate the large-fluctuation dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after the large fluctuations is characterized by a power law, and the exponents $p_\pm$ usually vary with the strength of the lar…

Extends metric to Margulis spacetimes for convex properties.

In this article we analyse linear correlation and non-linear dependence of traded volume, $v$, of the 30 constituents of Dow Jones Industrial Average at different value scales. Specifically, we have raised $v$ to some real value $α$ or $β$, which introduces a bias for small ($α, β<0$) or large ($α, β>1$) values. Our r…

In recent years, correntropy has been seccessfully applied to robust adaptive filtering to eliminate adverse effects of impulsive noises or outliers. Correntropy is generally defined as the expectation of a Gaussian kernel between two random variables. This definition is reasonable when the error between the two random…

This paper introduces constrained mixtures for continuous distributions, characterized by a mixture of distributions where each distribution has a shape similar to the base distribution and disjoint domains. This new concept is used to create generalized asymmetric versions of the Laplace and normal distributions, whic…

Enhances reinforcement learning with partial state information.

This paper studies gradient flows in asymmetric metric spaces and proves existence results.

Paper defines saddle points in asymmetric Dynkin games using martingale theory.

Study uncovers new phase transitions in asymmetric causal inference scenarios.

Modified asymmetric hidden Markov models for time series with autoregressive components.

Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…