arXiv research

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

Introduces an asymmetric model for measuring market risk.

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

New method uses asymmetric Tsallis relative entropy for better risk assessment in financial portfolios.

New asymmetric metric on Teichmüller space for surfaces.

New method improves portfolio allocation using local Gaussian correlation.

Machine learning improves beta forecasts, enhancing equity valuation and portfolio performance.

Study proves value of non-Markovian games with partial, asymmetric info.

We highlight several analogies between the Finsler (infinitesimal) properties of Teichmüller's metric and Thurston's asymmetric metric on Teichmüller space. Thurston defined his asymmetric metric in analogy with Teichmüllers' metric, as a solution to an extremal problem, which consists, in the case of the asymmetric me…

Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes i…

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

This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put tra…

Paper studies asymmetric matrix sensing, proving gradient descent converges to low-rank solutions.

Quantum networks learn task-dependent asymmetric similarity measures.

TSVQR captures heterogeneous and asymmetric data using quantile regression.

We consider classification in the presence of class-dependent asymmetric label noise with unknown noise probabilities. In this setting, identifiability conditions are known, but additional assumptions were shown to be required for finite sample rates, and so far only the parametric rate has been obtained. Assuming thes…

We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under jointly elliptic distributions of returns. Portfolio optimization of the Sharpe ratio is then explored, with an active-set algorithm presented for markets prohibiting sh…

We present the first provably sublinear time algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). Our proposal is also the first hashing algorithm for searching with (un-normalized) inner product as the underlying similarity measure. Finding hashing schemes for MIPS was considered hard. We formally sho…

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…

Study finds time-varying volatility and multifractality in Bitcoin, with asymmetry weakening as market efficiency increases.

Novel model improves clinical risk prediction by transferring knowledge between tasks over time.

The earthquake flow is asymmetric and cannot be extended to an SL(2,R) action.

Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap (i.e., inner product between binary vectors) or set containment. Minhash has inherent bias towards sma…

This survey is an introduction to the geometry of co-Minkowksi space, the space of unoriented spacelike hyperplanes of the Minkowski space. Affine deformations of cocompact lattices of hyperbolic isometries act on it, in a way similar to the way that quasi-Fuchsian groups act on hyperbolic space. In particular, there i…

This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support ou…

Stein discrepancy improves UDA performance in low-data scenarios.

APGD algorithm reconstructs point set from partial distance measurements.

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

New metrics derived from Hölder distortion on Hitchin components.

We show that the length function of a measured geodesic lamination is convex in Thurston's shear coordinates over Teichmüller space and strictly convex for generic laminations. We give some consequences of this result in the context of Thurston's asymmetric metric on Teichmüller space.

Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classifiers trained on images from one scanner to segment images from other scanners. We propose a transfer learning classifier that adapts to diff…

The Teichmüller space $\mathcal{T}(Σ)$ of a surface $Σ$ is equipped with Thurston's asymmetric metric. Stretch lines are oriented geodesics for this metric on $\mathcal{T}(Σ)$. We give the asymptotic behavior of the lengths of the measured geodesic laminations as one follows a stretch line in the positive direction.

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

Generalizes Thurston's asymmetric metric to flat metrics.

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

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 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.

Asymmetric expansion preserves convexity in hyperbolic geometry.

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

New core inflation measure predicts future headline inflation.

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 generalise the description of the dynamics of the order book of financial markets in terms of a Brownian particle embedded in a fluid of incoming, exiting and annihilating particles by presenting a model of the velocity on each side (buy and sell) independently. The improved model builds on the time-averaged number …

Bayesian VI copula models capture asymmetric intraday equity dependence.

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