arXiv research

This research improves DNN defense by profiling and analyzing effective paths.

The study examines insurance demand under rough volatility and path-dependent shocks.

Temporal coarse-graining of latent default paths explains effective correlation in corporate defaults.

Temporal aggregation reveals latent default correlation from monthly data.

New methods estimate causal effects through mediators, handling confounding without strict assumptions.

ScoreMatchingRiesz improves debiased machine learning and policy effects estimation.

Collective classification has been intensively studied due to its impact in many important applications, such as web mining, bioinformatics and citation analysis. Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simulta…

PathRank ranks paths in spatial networks using multi-task learning.

Study shows how feature weighting affects neural network regularization.

In this paper we analytically study the problem of pricing an arithmetically averaged Asian option in the path integral formalism. By a trick about the Dirac delta function, the measure of the path integral is defined by an effective action functional whose potential term is an exponential function. This path integral …

Proposes ANN for more accurate path loss prediction in urban environments.

The paper develops fair machine learning models using causal path-specific effects.

The study identifies volatility models from path geometry using signature-based methods.

New framework for attributing online marketing touchpoints.

One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that explo…

Predicts path failures in evolving networks using deep learning.

Study of most probable paths for anisotropic Brownian motions on manifolds.

Dupire's functional Itô calculus provides an alternative approach to the classical Malliavin calculus for the computation of sensitivities, also called Greeks, of path-dependent derivatives prices. In this paper, we introduce a measure of path-dependence of functionals within the functional Itô calculus framework. Name…

Update rules for learning in dynamic time warping spaces are based on optimal warping paths between parameter and input time series. In general, optimal warping paths are not unique resulting in adverse effects in theory and practice. Under the assumption of squared error local costs, we show that no two warping paths …

This paper presents a new methodology to compute first-order Greeks for barrier options under the framework of path-dependent payoff functions with European, Lookback, or Asian type and with time-dependent trigger levels. In particular, we develop chain rules for Wiener path integrals between two curves that arise in t…

PWSHAP provides targeted explanations for complex models.

Study finds racial bias in pulse oximeter readings has minimal impact on ICU ventilation rates.

We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that…

LOV model calibrates European and American options with path-dependent volatility.

Paper bridges ResNet and Feynman path integral for mathematical understanding.

Framework uses optimal transport to quantify model risk in stochastic path laws.

Develops methods to simulate rare transitions in molecular systems.

New model predicts implied volatility using past asset price paths.

We introduce a new method to measure model risk using optimal transport on path signatures.

New method generates realistic financial price paths with drawdowns.

ie-HGCN addresses HIN challenges by efficiently learning node representations.

In regression settings where explanatory variables have very low correlations and there are relatively few effects, each of large magnitude, we expect the Lasso to find the important variables with few errors, if any. This paper shows that in a regime of linear sparsity---meaning that the fraction of variables with a n…

The path integral generalization of the Casson invariant as developed by Rozansky and Witten is investigated. The path integral for various three manifolds is explicitly evaluated. A new class of topological observables is introduced that may allow for more effective invariants. Finally it is shown how the dimensional …

New algorithmic view of ℓ2 regularization using ODEs and path-following methods.

Efficiently computes sparse signature coefficients using kernels.

Physics methods improve derivatives pricing accuracy and efficiency.

This work explores functional expansions to handle path dependence in various fields.

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects …

SLEM uses machine learning to improve causal inference from observational data.

SigMA uses signatures and attention to estimate parameters in fBm-driven SDEs.

Proposes a method to learn fair classifiers without restrictive assumptions.

Novel signature approach for pricing and hedging path-dependent options with market frictions.

Improved particle pricing methods for path-dependent options.

Research uses CPS to estimate uncertainty in ML radio metric models.

A new slicing method speeds up sliced Wasserstein estimation.

A new path gradient estimator speeds up normalizing flows without sacrificing accuracy.

Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random …

CDA framework infers channel influence from aggregated data without user identifiers.