arXiv research

Graph neural networks improve El Niño forecasts.

Graph Neural Networks improve El Niño forecasts.

Study improves seasonal forecasts using deep learning.

Paper models spatio-temporal extremes using conditional variational autoencoders.

Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti…

Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.

We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific O…

Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostl…

M-CaStLe discovers causal structures in multivariate space-time data.

GOAL algorithm reduces and rotates feature space for small data classification.

Novel algorithm detects causal macrovariables from high-dimensional data.

Functional data analysis is a statistical framework where data are assumed to follow some functional form. This method of analysis is commonly applied to time series data, where time, measured continuously or in discrete intervals, serves as the location for a function's value. Gaussian processes are a generalization o…

Machine learning improves sub-seasonal climate forecasting, especially gradient boosting and deep learning.

The study uses machine learning to predict CAT bond coupons based on climate data.

TreeDOX predicts chaotic systems without hyperparameter tuning.

Improved NiNo networks accelerate Adam training by up to 50%.

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few p…

Parameters defined via General Estimating Equations (GEE) can be estimated by maximizing the Empirical Likelihood (EL). Newey and Smith (2004) have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its O(n^-1) bias is small and that bias-corrected EL is higher-ord…

Parameters defined via general estimating equations (GEE) can be estimated by maximizing the empirical likelihood (EL). Newey and Smith [Econometrica 72 (2004) 219--255] have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties, namely, that its $O(n^{-1})$ bias is small and that …

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where t…

Develops an empirical likelihood framework for random forests and ensembles.

A new AI framework reduces costs and improves performance.

Deep learning models learn chaotic system dynamics from real and simulated data.

Introduces BPEL for EL, enhancing flexibility and using MCMC for inference.

In this paper we study the boundary at infinity of the curve complex $\mathcal{C}(S)$ of a surface $S$ of finite type and the relative Teichmüller space $\mathcal{T}_{el}(S)$ obtained from the Teichmüller space by collapsing each region where a simple closed curve is short to be a set of diameter 1. $\mathcal{C}(S)$ an…

The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses however is barely covered. So far there are n…

Generalisation of the El Farol bar problem to that of many bars here leads to the Kolkata restaurant problem, where the decision to go to any restaurant or not is much simpler (depending on the previous experience of course, as in the El Farol bar problem). This generalised problem can be exactly analysed in some limit…

This paper presents practical methods for portfolio selection in investments.

We show that if S is a finite type orientable surface of genus g and p punctures where 3g+p > 4, then EL(S) is (n-1)-connected and (n-1)-locally connected where dim(PML(S))=2n+1=6g+2p-7. Furthermore, if g=0, then EL(S) is homeomorphic to the p-4 dimensional Nobeling space.

Efficiently estimates uncertainty for LLM-based entity linking in tabular data.

Statistical inference of analytically non-tractable posteriors is a difficult problem because of marginalization of correlated variables and stochastic methods such as MCMC and VI are commonly used. We argue that stochastic KL divergence minimization used by MCMC and VI is noisy, and we propose instead EL_2O, expectati…

The objective of this paper is to provide a comprehensive study no-arbitrage pricing of financial derivatives in the presence of funding costs, the counterparty credit risk and market frictions affecting the trading mechanism, such as collateralization and capital requirements. To achieve our goals, we extend in severa…

We study the geometric nature of the Jacobi equation. In particular we prove that Jacobi vector fields (JVFs) along a solution of the Euler-Lagrange (EL) equations are themselves solutions of the EL equations but considered on a non-standard algebroid (different from the tangent bundle Lie algebroid). As a consequence …

OL4EL optimizes edge learning on resource-constrained servers.

A new oscillator measures trending behavior of financial instruments.

Minimal vector fields on oscillator groups studied, with specific conditions for minimality.

Study examines boundedness of oscillating singular integrals on specific Lie groups.

Log-periodic oscillations have been used to predict price trends and crashes on financial markets. So far two types of log-periodic oscillations have been associated with the real markets. The first type are oscillations which accompany a rising market and which ends in a crash. The second type oscillations, called "an…

Estimates box dimension of fractal interpolation surfaces using oscillation vectors.

Researchers classify lattices in a specific four-dimensional group.

The Duffing oscillator's parameters are identified online using variational message passing.

New method linearizes nonlinear coupled oscillators on graphs.

The present paper introduces a majority orienting model in which the dealers' behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of…

The study derives generalization bounds for neural oscillators, improving their performance with regularization.

Study finds the spectrum of a cubic Dirac operator on specific oscillator group manifolds.

Study on black hole interiors with matter fields, showing oscillation condition impacts blow-up.

EL framework certifies and flags bias in ML models without distributional assumptions.

We study geometric quantization of the harmonic oscillator in terms of a singular real polarization given by fibres of the energy momentum map.