arXiv research

The report evaluates heuristics for learning timescale graphical event models.

The paper refines and generalizes worst-case law invariant convex risk measures.

Refines d'Alembertian for signed Lorentz distance functions in metric measure spacetimes.

We refine and generalize several interpolation inequalities bounding the $L^p$ norm of a probability density with respect to the reference measure $μ$ by its Sobolev norm and the Kantorovich distance to $μ$ on a smooth weighted Riemannian manifold satisfying $CD(0, \infty)$ condition.

New bounds for knot distances using Khovanov homology.

PG-EVIKAL refines molecular property predictions using neighbor fusion and evidential neural networks.

LMC achieves sqrt(d) dependence in sampling error, improving previous bounds.

Improved anti-cancer drug sensitivity prediction using REFINED CNN ensemble learning.

This paper investigates methods for quantifying similarity between audio signals, specifically for the task of of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantised au…

Study weak super Ricci flow through neckpinch in metric measure spaces.

This paper refines bounds on random walk speed in Teichmüller space.

New measures generalize existing ones, linking information and risk.

Refined theorem on linear perturbations with applications in singularity theory and optimization.

Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.

ABI bypasses likelihood intractability with nonparametric distribution matching.

Regularity properties of intrinsic objects for a large class of Stein Manifolds, namely of Monge-Ampère exhaustions and Kobayashi distance, is interpreted in terms of modular data. The results lead to a construction of an infinite dimensional family of convex domains with squared Kobayashi distance of prescribed regula…

This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring the validity of the produced knockoffs is optimized; this criterion i…

Paper introduces robust market making using Wasserstein distance and entropy regularization.

Let $P, Q$ be Heegaard surfaces of a closed orientable 3-manifold. In this paper, we introduce a method for giving an upper bound of Hempel distance of $P$ by using the Reeb graph derived from a certain horizontal arc in the ambient space $[0,1]\times[0,1]$ of the Rubinstein-Scharlemann graphic derived from $P$ and $Q$…

Algorithm recovers causal graphs from data with fewer tests.

Refines knot defect measurement in 3D and 4D.

Generates infinite-depth hierarchical clusters from few examples.

The concept of refinement from probability elicitation is considered for proper scoring rules. Taking directions from the axioms of probability, refinement is further clarified using a Hilbert space interpretation and reformulated into the underlying data distribution setting where connections to maximal marginal diver…

This thesis uses Kantorovich-Rubinstein distance for classifying points based on their measures.

Study non-orientable link cobordisms using Floer homologies to prove inequalities.

kdiff measures distances for time series and structured data.

We improve density-based distances using normalizing flows and score matching.

Counting lattice points in moduli space of Klein surfaces.

We consider various notions of strains; quantitative measures for the deviation of a linear transformation from an isometry. The main approach, which is motivated by physical applications and follows the work of Patrizio Neff and co-workers , is to select a Riemannian metric on $\text{GL}_n$, and use its induced geodes…

Measuring conditional independence is one of the important tasks in statistical inference and is fundamental in causal discovery, feature selection, dimensionality reduction, Bayesian network learning, and others. In this work, we explore the connection between conditional independence measures induced by distances on …

Upper bound for max-sliced 2-Wasserstein distance between measures.

We propose three measures of mutual dependence between multiple random vectors. All the measures are zero if and only if the random vectors are mutually independent. The first measure generalizes distance covariance from pairwise dependence to mutual dependence, while the other two measures are sums of squared distance…

Paper develops multivariate time series similarity and distance measures.

New tools for estimating and inferring Wasserstein distance in topic models.

The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visua…

The paper introduces a statistical test to assess and rank distance measures.

A new framework tightens risk measure confidence bounds.

This work extends stochastic localization to joint probability measures for data analysis.

The paper introduces a new Wasserstein distance for approximating posteriors in inverse problems.

ATD measures language distance using neural models, recovering linguistic groupings.

Study entropic regularization of Gaussian measures and processes on Hilbert space.

Paper estimates Wasserstein distance for Ricci shrinkers.

LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.

We initiate the rigorous study of classification in quasi-metric spaces. These are point sets endowed with a distance function that is non-negative and also satisfies the triangle inequality, but is asymmetric. We develop and refine a learning algorithm for quasi-metrics based on sample compression and nearest neighbor…

Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, we focus on mixture distributions that arise naturally in several application domains where the data contains different sub-populations. For …

Unified understanding of neural representation similarity measures.

Standardized fairness measures for continuous risk scores using Wasserstein distance.

Study geodesic distances and convexity in contact sets.