arXiv research

Two ML approaches learn local volatility surfaces from option prices, with GP being arbitrage-free.

In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken Language Understanding and user experience even if new locales do not have sufficient data and 2) reduce the cost of scaling the domain cla…

New GP model estimates piecewise continuous functions.

We study the pricing and hedging of derivatives in incomplete financial markets by considering the local risk-minimization method in the context of the benchmark approach, which will be called benchmarked local risk-minimization. We show that the proposed benchmarked local risk-minimization allows to handle under extre…

MD-split+ creates locally valid prediction regions for complex data.

Unified approach to characterize and regularize deep neural network local minima.

New method detects drift in high-dimensional data.

Develops a deep learning method for enforcing no-arbitrage in local volatility surfaces.

Local-HDP learns independent topics for each 3D object category in real-time.

One of the limiting factors of using support vector machines (SVMs) in large scale applications are their super-linear computational requirements in terms of the number of training samples. To address this issue, several approaches that train SVMs on many small chunks of large data sets separately have been proposed in…

SDSPCAAN combines supervised and local data structures for better dimensionality reduction.

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a …

The commuting vector fields approach, devised for strichartz estimates in [13], was developed for proving the local well-posedness in the Sobolev spaces $H^s$ with $s>2+\frac{2-\sqrt{3}}{2}$ for general quasi-linear wave equation in ${\mathbb R}^{1+3}$ by Klainerman and Rodnianski. Via this approach they obtained the l…

Paper tackles cybersecurity attack detection with an ensemble approach.

This paper presents a new approach for Gaussian process (GP) regression for large datasets. The approach involves partitioning the regression input domain into multiple local regions with a different local GP model fitted in each region. Unlike existing local partitioned GP approaches, we introduce a technique for patc…

We give a new short self-contained proof of the result of Opozda [B. Opozda, A classification of locally homogeneous connections on 2-dimensional manifolds, Differential Geom. Appl. 21 (2004), 173-198.] classifying the locally homogeneous torsion free affine surfaces and the extension to the case of surfaces with torsi…

Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovati…

This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward …

New insights into bias-variance tradeoff for data-driven optimization under local misspecification.

DNNs improve localization from channel estimates, overcoming practical impairments.

The massive amount of available data potentially used to discover patters in machine learning is a challenge for kernel based algorithms with respect to runtime and storage capacities. Local approaches might help to relieve these issues. From a statistical point of view local approaches allow additionally to deal with …

Paper introduces conformal prediction for reliable uncertainty quantification in landmark localization.

Personalized federated learning improves model accuracy and fairness by leveraging shared representations and local memorization.

In the context of a locally risk-minimizing approach, the problem of hedging defaultable claims and their Follmer-Schweizer decompositions are discussed in a structural model. This is done when the underlying process is a finite variation Levy process and the claims pay a predetermined payout at maturity, contingent on…

New approach to adaptively select bandwidths in nonparametric regression.

Local gluing connects flow lines in finite time intervals.

We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner. Our approach naturally operates over structured data and tailors the predictor, functionally, towards a chosen family of (local) witnesses. The estimation problem is setup as a co-operative game between an …

Proposes methods to improve multi-label learning by addressing local label imbalance.

Overlap-Local-SGD improves distributed SGD by overlapping communication and computation.

The article calculates the near horizon limit of Wang--Yau quasi-local mass.

Homological algebra used to study local equivalence of complex rings.

LIMIS improves locally interpretable models by selecting and distilling key instances.

In this article, we discuss the local rigidity of Clifford-Klein forms of homogeneous spaces of 1-connected completely solvable Lie groups. In fact, we introduce a splitting of the local rigidity: vertical rigidity and horizontal rigidity. By using this splitting, we refine some existing results about the local rigidit…

LCMQR improves prediction intervals by adapting to local heteroscedasticity.

New approach combines PCA and t-sne for better data analysis.

A new TwinGP framework for efficient large-scale GP modeling.

New algorithms adaptively compete against complex environments with local regularities.

As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we p…

Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems. We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specific…

New approach improves computational efficiency of Bass Local Volatility model.

Paper presents a novel approach for global feature aggregation in Graph Neural Networks.

Novel approach combines local and global brain changes for AD prediction.

BOinG optimizes HPO problems by focusing on promising local regions.

Neural network tackles continual learning with neuromodulation and local error signals.

This thesis explores GNNs, categorizing them into local and global approaches.

Method calibrates local volatility and stochastic short rate models for equity-rate dynamics.

In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was taken so far to extract local features efficiently for a whole image. In this paper, we present an approach to compute patch-based local feature…

Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box. This paper highlights the importance of defining the right locality, the neighborhood on which a local su…