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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,291 papers · 148 categories

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118236354472 · Jun 202019922001200920182026
48 results for independence system

The paper develops robust tests for detecting independence in synchronous stochastic systems with finite sample guarantees.

problem Detecting independence in synchronous stochastic systems with finite sample guarantees.
method Combines confidence region estimates with permutation tests and dependence measures to detect nonlinear dependence.
result Consistent hypothesis tests for detecting independence under mild assumptions.

Method discovers local independence in systems with continuous variables.

problem Applying Context-Specific Independence (CSI) to continuous variables is impractical.
method Neural contextual decomposition (NCD) learns partition of joint outcome space.
result NCD successfully discovers local independence in synthetic and real-world systems.

Paper develops metrics for random dynamical systems using vector-valued RKHSs.

problem Creating metrics for random nonlinear dynamical systems.
method Develops metrics on random dynamical systems using Perron-Frobenius operators in vector-valued reproducing kernel Hilbert spaces (vvRKHSs). Uses operator-valued kernels and time-wise independence criteria.
result Extends existing metrics for deterministic systems and introduces kernel maximal mean discrepancy for random processes.

SpeakerStew verifies 46 languages with reduced training and inference costs.

problem Speaker verification for 46 languages with smart speaker interactions.
method Pooling multilingual data, triage between text-dependent and text-independent models.
result Training on multiple languages generalizes well and reduces computational requirements.

New methods infer causal structure from data without hidden variables.

problem Inferring causal structure from observational data with hidden variables.
method Introduces alternative independence tests and conditionally-additive-noise models.
result Can infer causal relations without assumptions about equation form or hidden variables.

We propose a method to clean covariance matrices of nonstationary systems by using time-independent eigenvalues.

problem Noise in covariance matrices of nonstationary systems with time-independent eigenvalues.
method Data-driven approach to use independent eigenvalues encoding long-term influence of future on present.
result Our method outperforms optimal stationary methods for filtering covariance matrix and its inverse.

This work develops a non-parametric test for relational independence in non-i.i.d. data.

problem Testing independence in relational systems where data samples are not i.i.d.
method Kernel mean embedding for relational variables, consistent non-parametric scalable kernel test.
result Empirically validated effectiveness compared to state-of-the-art tests.

New research challenges the independence assumption in neurosymbolic learning, leading to overconfident predictions and unrepresentable uncertainty.

problem The independence assumption in neurosymbolic learning systems can lead to overconfident predictions and hinder uncertainty quantification.
method The study proves the limitations of the independence assumption and introduces new loss functions that are non-convex and difficult to optimise.
result Neurosymbolic learning systems using the independence assumption are prone to overconfidence and cannot represent uncertainty over multiple valid options.

This study provides a new mathematical structure for Koopman eigenfunctions.

problem Understanding and representing nonlinear dynamics as linear.
method Theoretical, analytical, and numerical approaches to Koopman eigenfunction space.
result Equivalence of minimal generating set and maximal independent set, defining conditions for independence.

New method uses sufficient statistics to infer causal relationships from observational data.

problem Inferring causal relationships from observational data with hidden variables.
method Information Bottleneck method applied to find functional sufficient statistics.
result New causal rules not obtainable from standard methods, validated on simulated and real data.

New ICA method improves on existing techniques.

problem Finding independent components in data.
method Multiple-weighted Independent Component Analysis (MWeICA) based on approximate diagonalization of weighted covariance matrices.
result MWeICA achieves better results than state-of-the-art ICA methods with similar computational time.

Study on equilibrium points of dynamical systems with multiple integrals.

problem Understanding the equilibrium points of dynamical systems with multiple independent first integrals.
method Analyzes the equilibrium locus as a smooth manifold and fiber bundle with a natural connection.
result Parallel transport exists for the connection and can measure eigenvalue variations.

Study shows increased VRE penetration reduces electricity prices and volatility.

problem Impact of increased variable renewable energy on electricity prices and volatility.
method Hourly, real-time data from six ISOs, quantile and skew t-distribution regressions.
result Increased VRE penetration is associated with decreased system electricity price and volatility in most ISOs.

We generalize the classical Lie results on a basis of differential invariants for a one-parameter group of local transformations to the case of arbitrary number of independent and dependent variables. It is proved that if universal invariant of a one-parameter group is known then a complete set of functionally independ…

2001-12-24abs ↗pdf ↗

Two impossibility theorems show formal alignment certification is impossible for AI systems.

problem Formal certification of AI alignment over open-ended domains is impossible.
method Two independent impossibility theorems: Semantic and Statistical barriers.
result No procedure can simultaneously satisfy soundness, completeness, and tractability.

AI triage and diagnostic system performs similarly to human doctors, with safer triage advice.

problem Improving patient care through more reliable symptom checkers.
method Prospective validation study comparing AI system to human doctors.
result AI system's accuracy in identifying conditions comparable to human doctors, with safer triage advice.

Kernel-based tests detect dependencies in multivariate time series, including stationary and non-stationary data.

problem Detecting dependencies in multivariate time series data, especially non-stationary data.
method Kernel-based statistical tests of joint independence, extending dHSIC to handle both stationary and non-stationary processes.
result Robustly uncovers significant higher-order dependencies in synthetic and real-world data.

Study identifies and validates a method for system identification of Markov jump linear systems.

problem System identification for autonomous Markov jump linear systems with complete state observations.
method Proposes switched least squares method for identification and derives rates of convergence.
result Data-independent rate of convergence is O(log(T)/T)\mathcal{O}\big(\sqrt{\log(T)/T} \big), showing strong consistency.

Redundancy in AI perception systems doesn't guarantee independent error occurrences.

problem Lack of direct statistical evidence of super-human automated driving performance.
method Investigated the effectiveness of redundancy in neural networks for independent error occurrences.
result Errors in neural networks for computer vision tasks are correlated, not independent.

We establish causal semantics for SDEs and develop methods to reason about them.

problem Understanding causal relationships in systems modeled by stochastic differential equations.
method We introduce a causal graph framework, Markov properties, and do-calculus for SDEs.
result We prove the σσ-separation Markov property and do-calculus for causal SDEs.

This work concerns the definition and analysis of a new class of Lie systems on Poisson manifolds enjoying rich geometric features: the Lie--Hamilton systems. We devise methods to study their superposition rules, time independent constants of motion and Lie symmetries, linearisability conditions, etc. Our results are i…

2012-11-29abs ↗pdf ↗

The paper analyzes systemic risk in an insurance model with multiple business lines and heterogeneous claims.

problem Analyzing systemic risk in a multi-dimensional insurance model with heterogeneous claims.
method A multi-dimensional Lévy process-based renewal risk model with pairwise asymptotic independence (PAI).
result Asymptotic formulas for tail probabilities and systemic risk measures are derived.

Flat systems of up to 2 dimensions have flat subsystems.

problem Characterizing flat subsystems in flat systems of differential dimension 2.
method Analyzing subsystems of a flat system of differential dimension at most 2.
result Flat subsystems of a flat system of differential dimension at most 2 exist and can have independent time-uniform outputs.

Study of supervised learning from multiple non-independent sequences.

problem Efficient learning from many non-independent sequences.
method Generalizes conditions for efficient learning from independent examples and single auto-correlated sequences.
result Error rate changes from Θ(n/mT)Θ(n / m T) to Ω(n2/m2T)Ω(n^2 / m^2 T) as the number of trajectories increases.

This paper uses MIS to identify key financial institutions with minimal risk contagion.

problem Mitigating systemic risk during extreme financial events.
method Applying extreme value theory and MIS from graph theory to identify diversified portfolios.
result Identified a subset of institutions with minimal extremal dependence for diversified portfolios.

We briefly recall a fundamental exterior differential system introduced by the author and then apply it to the case of three dimensions. Here we find new global tensors and intrinsic invariants of oriented Riemaniann 3-manifolds. The system leads to a remarkable Weingarten type equation for surfaces on hyperbolic 3-spa…

2015-04-17abs ↗pdf ↗

Neurosymbolic predictors fail to model uncertainty under independence assumption.

problem Neurosymbolic predictors' reliance on independence assumption limits their ability to model uncertainty.
method Formal analysis of NeSy predictors under independence assumption.
result Assuming independence among symbolic concepts prevents NeSy predictors from representing uncertainty.

We study mechanical systems subject to constraint functions that can be dependent at some points and independent at the rest. Such systems are modelled by means of generalized codistributions. We discuss how the constraint force can transmit an impulse to the motion at the points of dependence and derive an explicit fo…

2000-08-18abs ↗pdf ↗

DOODL learns shared spectral dynamics across related dynamical systems.

problem Learning independent dynamical operators for each system limits discovery of shared structure.
method DOODL learns a dictionary of characteristic spectral dynamics on a manifold of related systems.
result DOODL achieves errors one to two orders of magnitude lower than independent operator estimation methods.

New integrable systems constructed for non-diagonal Killing tensors.

problem Constructing integrable Hamiltonian systems with quadratic momenta.
method Using Nijenhuis geometry and gl-regular Nijenhuis operators.
result Reproduces classical Stäckel construction and finds new systems for n≥3.

In this paper, the symmetry group of a differential system of n quadratic homogeneous first order ODEs of n variables is studied. For this purpose, we consider the action of both point and contact transformations to signify the corresponding Lie algebras. We also find the independent differential invariants of these ac…

2008-07-05abs ↗pdf ↗

In this paper, we show how to reduce the Penrose conjecture to the known Riemannian Penrose inequality case whenever certain geometrically motivated systems of equations can be solved. Whether or not these special systems of equations have general existence theories is therefore an important open problem. The key tool …

2009-05-15abs ↗pdf ↗

Theorems adapted for prequantum systems, showing symplectomorphism and gauge transformation.

problem Adapting classical theorems to prequantum systems.
method Establishing analogs of the Darboux, Moser, and Weinstein theorems.
result Prequantum systems with vanishing first cohomology are equivalent up to symplectomorphism and gauge transformation.