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.
New method for interpreting non-linear models using forward marginal effects.
Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matrix factorization, which predicts unobserved entries as linear combinations of latent variables. We g…
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction func…
Paper models non-linear dynamics from time series data.
RNN-HAR model improves VaR forecasting with long-memory and non-linear dynamics.
Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientif…
fmeffects package interprets non-linear models in plain language.
The goal of a recommendation system is to predict the interest of a user in a given item by exploiting the existing set of ratings as well as certain user/item features. A standard approach to modeling this problem is Inductive Matrix Completion where the predicted rating is modeled as an inner product of the user and …
Method estimates mixture components without discretizing parameters.
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional n…
TaCo prevents non-linear classifiers from detecting sensitive attributes.
DeepKriging uses DNNs to predict spatial data with improved accuracy and scalability.
GP-KAN uses Gaussian Processes in KANs for robust, parameter-efficient non-linear modeling.
The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution of some real-world phenomenon over time, for example models of the climate or the functioning of the human brain. The models we are interest…
We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We fin…
Study evaluates machine learning for predicting treatment effects in observational studies.
Develops inverse extended Kalman filter for predicting adversarial steps.
Enhances Cox model for survival analysis with symbolic non-linear log-risk functions.
Estimates signals from a continuous dictionary with sparse mixtures using optimization.
FFCP improves FCP's speed without sacrificing accuracy.
Proposes a deep neural network for predicting survival times with cure fractions.
PatternLocal improves XAI for non-linear models by suppressing suppressor variables.
Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods. Motivated by the powerful framework of Hilbert space embeddings o…
Heterosis is the improved or increased function of any biological quality in a hybrid offspring. We have studied yet the largest maize SNP dataset for traits prediction. We develop linear and non-linear models which consider relationships between different hybrids as well as other effect. Specially designed model prove…
Dynamic linear models improve travel time prediction for congested freeways.
Simplifies deep learning scaling analysis without sacrificing accuracy.
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates. This includes problems where one wishes to optimize a non-decomposable performance metric such as the F-measure or G-mean, and constrained training problems where the classifier needs to sati…
This paper reviews transfer learning for financial data predictions, highlighting its potential.
Paper proposes a novel SVM method for creating survival trees.
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to …
Unified Bayesian framework predicts cryptocurrency market dynamics and volatility.
Tree-based machine learning models such as random forests, decision trees, and gradient boosted trees are the most popular non-linear predictive models used in practice today, yet comparatively little attention has been paid to explaining their predictions. Here we significantly improve the interpretability of tree-bas…
Improved disability insurance model with collective health claims.
Develops inverse EKF for non-linear systems with stability guarantees and learning unknown dynamics.
Machine learning identifies ESG patterns for better stock selection.
New approach predicts stock price synchronization using RNNs and LSTMs.
This study compares two neural models for financial forecasting, showing their superiority.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
ParamBoost uses gradient boosting to create interpretable non-linear models with constraints.
DISTANA predicts and denoises spatial wave dynamics.
New method adds all interactions in non-linear models without high computational cost.
High-risk domains require reliable confidence estimates from predictive models. Deep latent variable models provide these, but suffer from the rigid variational distributions used for tractable inference, which err on the side of overconfidence. We propose Stochastic Quantized Activation Distributions (SQUAD), which im…
Bayesian neural networks improve uncertainty quantification in non-linear dimensionality reduction.
Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the h…
Learning by integrating multiple heterogeneous data sources is a common requirement in many tasks. Collective Matrix Factorization (CMF) is a technique to learn shared latent representations from arbitrary collections of matrices. It can be used to simultaneously complete one or more matrices, for predicting the unknow…
In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N, the n…
Project forecasts liquidity withdrawal using machine learning models.