arXiv research

Paper tackles order-dependence in structure learning of multivariate regression chain graphs.

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network (FON), that is, the network th…

The paper analyzes cryptocurrency trading networks using pairwise and high-order dependencies.

We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first step of all these algorithms consists of the PC-algorithm. This algorithm is known to be order-d…

CSD improves goodness-of-fit testing for higher-order dependence.

This paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-ord…

Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However, in many practical applications such as online social networks, networks are massive, dynamic, and naturally streaming, where pairwise inter…

In this paper we consider rough differential equations on a smooth manifold $\left( M\right) .$ The main result of this paper gives sufficient conditions on the driving vector-fields so that the rough ODE's have global (in time) solutions. The sufficient conditions involve the existence of a complete Riemannian metric …

Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks have been successfully applied to graph structured data such as point cloud and molecular data. These networks oft…

This research tackles ordering latent variables in normalizing flows.

Paper quantifies how much machine learning models can be explained.

The paper shows how different geodesic flows on surfaces can be mapped to each other.

We solve minimal separator problems in AMP chain graphs and improve structure learning algorithms.

Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is a…

We identify and analyze statistical regularities and irregularities in the recent order flow of different NASDAQ stocks, focusing on the positions where orders are placed in the orderbook. This includes limit orders being placed outside of the spread, inside the spread and (effective) market orders. We find that limit …

Integrates CNN and GRU for precise stock market risk alerts.

Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. However, almost all the previous phenomenological studies involved only a long-ranged autocorrelation function, or disregarded the multi-scaling properties induced by potential…

New distributed EnKF method for non-sequential assimilation of large datasets.

The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on genotype must approximate this by simple additive effects. While recent models have relaxed this constraint to also account for pairwise in…

SDREM models complex network data with deep learning, improving link prediction.

The paper tackles long-context linear system identification with improved sample complexity bounds.

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

Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational cha…

New framework detects directional influence in multivariate time series.

Bayesian context trees capture complex dependencies in categorical sequences.

Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives. When these algorithms are only trained to minimize the training/test error, they could suffer from systematic discrimination against individuals based on their sensitive attributes…

Market makers continuously set bid and ask quotes for the stocks they have under consideration. Hence they face a complex optimization problem in which their return, based on the bid-ask spread they quote and the frequency at which they indeed provide liquidity, is challenged by the price risk they bear due to their in…

New score-based methods identify causal structures with latent variables.

Deep neural networks perform well due to optimization effects.

New algorithm reduces dynamic regret in time-varying movement costs.

The paper provides bounds for LSA with fixed stepsizes under random estimates.

Attributing forecast gaps to component models in complex model suites

The paper tackles attributing forecast gaps in complex model suites.

The paper provides bounds for the empirical angular measure and applies them to improve statistical learning in extreme regions.

The paper analyzes how machine learning models perform under covariate shift, especially when the feature shift in $x$ is larger than that in $y$.

Unified normative modeling for neuroimaging phenotypes using denoising diffusion models.

Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way - not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper…

Improved stochastic approximation method reduces residual error.

Enhanced Zika spread forecasting using topological data analysis.

Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These -- and many others -- represent perfect examples of the opportunities and difficulties presented by Big Data: the available information often arrives from a variety of sources and has divers…

Estimates natural parameters of p-tensor Ising models efficiently.