arXiv research

A new diffusion kernel framework for data analysis.

A new kernel for probability measures based on optimal transport.

Producing overlapping schemes is a major issue in clustering. Recent proposed overlapping methods relies on the search of an optimal covering and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of another meas…

This work explores the connection between distances and kernels for conditional independence.

New multivariate dependency measure using Gaussian kernel and copula.

This is an introduction to Wiener measure and the Feynman-Kac formula on general Riemannian manifolds for Riemannian geometers with little or no background in stochastics. We explain the construction of Wiener measure based on the heat kernel in full detail and we prove the Feynman-Kac formula for Schrödinger operators…

A new method for efficient diffusion geometry computation from data regions.

A faster alternative to ICA methods using randomized features.

Paper proposes a method to reuse models without raw data.

New calibration measures for multi-class classification improve model accuracy.

Efficiently explains model outputs using HSIC, a dependence measure.

In this paper we introduce a new multivariate dependence measure based on comonotonicity by means of product moment which motivated by the recent papers of Koch and Schepper (ASTIN Bulletin 41 (2011) 191-213) and Dhaene et al. (Journal of Computational and Applied Mathematics 263 (2014) 78-87). Some differences and rel…

Paper presents a new VMBQC model with fewer parameters for better generative modeling.

Dual representations for systemic risk measures using acceptance sets.

A liquidity measure based on consideration and price range is proposed. Initially defined for daily data, Liquidity Index (LIX) can also be estimated via intraday data by using a time scaling mechanism. The link between LIX and the liquidity measure based on weighted average bid-ask spread is established. Using this li…

New method uses kernel deviance measures to discover causal relationships in heterogeneous data.

Develops a kernel for financial time series analysis.

New algorithm reduces dynamic regret for MDPs with unknown transition and adversarial rewards.

New MBQC algorithm uses randomness for generative modeling.

A method improves Cryo-EM 3D map refinement by regularizing rotation estimation.

Paper proposes adjustments for ARI and AMI measures.

Motivation: Prediction of ligands for proteins of known 3D structure is important to understand structure-function relationship, predict molecular function, or design new drugs. Results: We explore a new approach for ligand prediction in which binding pockets are represented by atom clouds. Each target pocket is compar…

New algorithm tackles adversarial RL without horizon constraints.

MBQC linked to CQCA, yielding efficient Ansätze.

A new complexity measure MDL-COMP for overparameterized models improves generalization performance.

We derive formulas for F measures' standard error and confidence intervals.

Different approaches to defining dynamic market risk measures are available in the literature. Most are focused or derived from probability theory, economic behavior or dynamic programming. Here, we propose an approach to define and implement dynamic market risk measures based on recursion and state economy representat…

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a…

New study shows how model complexity affects test risk, challenging classical theory.

We characterize when a convex risk measure associated to a law-invariant acceptance set in $L^\infty$ can be extended to $L^p$, $1\leq p<\infty$, preserving finiteness and continuity. This problem is strongly connected to the statistical robustness of the corresponding risk measures. Special attention is paid to concre…

Introduces new performance measures using scaled utility functions.

Solves a problem related to dual Orlicz curvature measure.

The economy globalization measure problem is discussed. Four macroeconomic indices of twenty among the "richest" countries are examined. Four types of "distances" are calculated.Two types of networks are next constructed for each distance measure definition. It is shown that the globalization process can be best charac…

New properties for density-based dissimilarity measures in hybrid clustering are proposed and evaluated.

Paper develops a fractal dimension-based generalization measure.

Unified framework for studying softmax attention under large prompts.

ATS improves calibration of deep networks, especially for small validation sets and noisy labels.

New risk measure considers horizon risk and interest rate uncertainty.

New formula for instantaneous frequency in unbalanced systems.

Spectral risk measures are attractive risk measures as they allow the user to obtain risk measures that reflect their subjective risk-aversion. This paper examines spectral risk measures based on an exponential utility function, and finds that these risk measures have nice intuitive properties. It also discusses how th…

This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts. The FCD computes a similarity between two documents through an effective binary search on the intersection set between the …

Proposes a new tail risk measure based on the most probable maximum risk event size.

The inversion formula for conservative multifractal measures was unveiled mathematically a decade ago, which is however not well tested in real complex systems. In this Letter, we propose to verify the inversion formula using high-frequency turbulent financial data. We construct conservative volatility measure based on…

We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate …

Study shows optimal rates for estimating Wasserstein metric and measures.

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing …

In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …

We propose a generalization of the classical notion of the $V@R_λ$ that takes into account not only the probability of the losses, but the balance between such probability and the amount of the loss. This is obtained by defining a new class of law invariant risk measures based on an appropriate family of acceptance set…