The accurate characterization of the business cycles in the nonlinear dynamic financial and economic systems in the time of globalization represents a formidable research problem. The central banks and other financial institutions make their decisions on the minimum capital requirements, countercyclical capital buffer …
On-device research index
arXiv research
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.
169,181 papers · 148 categories
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48 results for “nonlinear interaction”
A new method quickly identifies key variables and interactions.
problem Identifying key variables and interactions in high-dimensional data.
method Kernel trick for sparse orthogonal decomposition in O(# covariates) time.
result Outperforms existing methods for large, high-dimensional data sets.
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
problem Lack of reliable inference for ML-discovered nonlinearities and interactions in epidemiological data.
method Combines Bayesian sparse regression, tree ensembles, and Shapley values.
result Valid uncertainty quantification for feature effects at the individual level.
Neural Granger Causalitystat.ML
Neural Granger causality detects nonlinear interactions in time series data.
problem Inconsistent estimation of Granger causal interactions using linear models in nonlinear systems.
method Structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) with sparsity-inducing penalties.
result Neural Granger causality methods outperform traditional approaches on the DREAM3 challenge data.
Study on identifying and inferring nonlinear dynamics on unknown networks.
problem Identifying network structure in nonlinear dynamic systems with unknown interactions.
method Showed network structure is not generically identified, requiring sufficient spectral heterogeneity. Developed necessary and sufficient conditions for identification and proposed a semiparametric estimator.
result Necessary and sufficient conditions for identification of network structure in nonlinear dynamic systems.
Inferring linear and nonlinear Interaction networks using neighborhood support vector machinesstat.ML
Paper proposes SVM-based methods for inferring interaction networks.
problem Modeling interaction between variables in time series and high dimensions.
method Two approaches: neighborhood SVM and restricted Bayesian network for time series.
result Efficiency demonstrated through simulations with linear and nonlinear data.
Locally Anisotropic Interactions: I. Nonlinear Connections in Higher Order Anisotropic Superspaceshep-th
Higher order anisotropic superspaces are constructed as generalized vector superbundles provided with compatible nonlinear connection, distinguished connection and metric structures.
New method recovers causal networks from short time-series data.
problem Inferring causal relationships from short time-series data in complex systems.
method Large-scale Nonlinear Granger Causality (lsNGC) approach.
result Captures meaningful interactions from limited observational data.
Dynamical thermalization and turbulence in social stratification modelscond-mat.stat-mech
Study chaotic dynamics in social stratification models leading to thermalization and turbulence.
problem Understanding social stratification dynamics through chaotic nonlinear systems.
method Modeling social network links with oscillators and energies, studying Hamiltonian evolution and nonlinear interactions.
result Chaotic dynamics leads to dynamical thermalization and Kolmogorov-Zakharov turbulence, with implications for wealth inequality.
Extends RRR to capture nonlinear interactions in multi-response regression.
problem Complex relationships in real-world data cannot be adequately modeled by linear interactions.
method Introduces Higher Order Reduced Rank Regression (HORRR) using tensor representations and Tucker decomposition.
result HORRR can capture nonlinear interactions in multi-response regression.
Paper improves neural interaction modeling using nonlinear Hawkes processes.
problem Inability of classic Hawkes process to model inhibitory interactions.
method Augmented auxiliary latent variables and EM algorithm for efficient inference.
result Demonstrates accurate and efficient estimation of neural interaction dynamics.
Study shows attention-style models learn pairwise interactions efficiently.
problem Learning pairwise interactions in attention-style models.
method Proved minimax rate of convergence for learning pairwise interactions.
result Minimax rate is independent of embedding dimension and token number.
This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original -dimensional feature vector is propose…
TRNN combines tensor geometry with neural network nonlinearity for HD data.
problem Modeling high-dimensional data with preserved tensor geometry and nonlinear interactions.
method Introduces TRNN that integrates tensor geometry and neural network nonlinearity.
result TRNN preserves tensor geometry while offering nonlinearity.
New models improve machine learning accuracy and transparency in finance.
problem Black-box machine learning models lack interpretability in regulated industries.
method Introducing generalized groves of neural additive models with clear feature categories and interactions.
result Generalized groves of neural additive models achieve high accuracy with predominantly linear and sparse nonlinear components.
New method infers nonlinear Granger causality from time series data.
problem Inferring nonlinear Granger causality from time series data.
method Statistical Recurrent Units (SRUs) for modeling nonlinear interactions.
result The proposed economy-SRU model outperforms existing models in inferring Granger causality.
Uniform bounds derived for nonlinear statistics.
problem Deriving uniform bounds for nonlinear statistics.
method Extended method to Gaussian and Rademacher complexities.
result Tight bounds for U-statistics and error functionals.
Proposes a new variable grouping approach to improve Bayesian additive regression tree (BART) performance.
problem Improving the predictive performance of BART by reducing nonlinear interactions.
method Variable grouping to identify and separate variables into groups with no nonlinear interactions.
result The proposed GBART method significantly outperforms classical approaches in synthetic and real data experiments.
Proposes a multilayer nonlinear semi-nonnegative matrix factorization for better recommendation.
problem Inaccurate user-item interaction modeling with classical matrix factorization.
method Multilayer nonlinear Semi-NMF approach for latent user and item representations.
result Proposed method achieves better generalization in prediction and comparable representation in clustering.
Paper uses machine learning to uncover nonlinear dynamics in CAT bond pricing.
problem Traditional linear models miss nonlinear relationships in CAT bond pricing.
method Advanced machine learning techniques applied to CAT bond transaction records.
result Machine learning enhances CAT bond pricing accuracy and reveals complex risk interactions.
We develop a new DTSM with nonlinearities using Gaussian Processes for better interest rate forecasting.
problem Linear DTSMs fail to capture nonlinear relationships between macroeconomic variables and interest rates.
method We propose a Gaussian Process-based sequential Monte Carlo estimation and forecasting scheme.
result Nonlinear models outperform linear ones in forecasting core inflation, leading to significant economic value gains.
Method discovers nonlinear relations from time series data.
problem Identifying directional relations from nonlinear interactions in time series.
method Minimum predictive information regularization method for deep learning.
result Substantially outperforms other methods for learning nonlinear relations.
Study optimal allocation in uncertain multi-armed bandits using Gittins' theorem.
problem Optimal allocation in uncertain multi-armed bandits.
method Theoretical analysis based on nonlinear expectations, with relaxation in optimality definition.
result Gittins' allocation index provides optimal choices under strong independence and relaxed optimality conditions.
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a…
MuLFA predicts drug interactions more accurately than existing methods.
problem Improving drug safety by predicting drug interactions.
method Proposes MuLFA, a factorization autoencoder that models nonlinear interactions between drug pairs.
result MuLFA outperforms state-of-the-art methods in predicting drug interactions.
We propose dynamical systems trees (DSTs) as a flexible class of models for describing multiple processes that interact via a hierarchy of aggregating parent chains. DSTs extend Kalman filters, hidden Markov models and nonlinear dynamical systems to an interactive group scenario. Various individual processes interact a…
Deep DPP model uses neural networks to learn kernel matrices for DPPs.
problem Limitations of DPPs in capturing nonlinear interactions and incorporating item metadata.
method Integrates a deep feed-forward neural network to learn the kernel matrix of DPPs.
result Deep DPP model improves predictive performance and outperforms baselines.
In a recent formulation of a quantum field theory of forward rates, the volatility of the forward rates was taken to be deterministic. The field theory of the forward rates is generalized to the case of stochastic volatility. Two cases are analyzed, firstly when volatility is taken to be a function of the forward rates…
Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental …
The study extends convergence guarantees for nonlinear TD learning, focusing on ReLU networks and reversibility.
problem Understanding convergence of nonlinear TD learning with function approximators.
method Analyzing TD(0) dynamics through nonlinear ODEs, considering function approximator geometry and reversibility.
result Global convergence to the true value function for well-conditioned function approximators in reversible environments.
The work proposes a geometric background of the theory of field interactions and strings in spaces with higher order anisotropy. Our approach proceeds by developing the concept of higher order anisotropic superspace which unifies the logical and mathematical aspects of modern Kaluza-Klein theories and generalized Lagra…
When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid and other nonlinear models need to be considered, for instance, when there are multi-scattering effect…
Tree-LIME explains deep learning models using decision trees.
problem Deep learning models are black boxes, making them hard to explain and prone to biases.
method Developed a Tree-LIME approach using decision trees to explain predictions of deep learning models.
result Tree-LIME can capture nonlinear interactions and creates more reliable explanations.
Global minimizers for fast diffusion versus nonlocal interactions on negatively curved manifoldsmath.AP
Study finds conditions for global minimizers on curved manifolds with fast diffusion and nonlocal interactions.
problem Existence of global minimizers for a free energy functional on negatively curved manifolds.
method Investigation of Carlson-Levin type inequalities for Cartan-Hadamard manifolds.
result Establishes necessary and sufficient conditions for the existence of global energy minimizers.
CDSSL improves representation quality by integrating linear and nonlinear dependencies.
problem Scarcity of labeled data and neglect of nonlinear dependencies in SSL.
method CDSSL combines linear correlations and nonlinear dependencies using HSIC in RKHS.
result CDSSL enhances representation quality on diverse benchmarks.
Tensor decomposition is an important technique for capturing the high-order interactions among multiway data. Multi-linear tensor composition methods, such as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the complex interactions among objects are multi-linear, and are thus insufficient to repres…
We present an introduction to the geometry of higher order vector and co--vector bundles (including higher order generalizations of the Finsler geometry and Kaluza--Klein gravity) and review the basic results on Clifford and spinor structures on spaces with generic local anisotropy modeled by higher order nonlinear con…
Neural networks learn to mimic brain neurons with two-input activation functions, improving performance and robustness.
problem Training neural networks to mimic the complex interactions of brain neurons.
method Developed a network-in-network architecture with two-input activation functions, optimized hyperparameters, and compared to conventional ReLU networks.
result Two-input activation functions can learn soft XOR functions, improving network performance and robustness.
Interpretable deep convolutional model for nonlinear multivariate time series in complex systemsstat.ML
DCIts interprets complex time series data with interpretable coefficients.
problem Interpreting nonlinear multivariate time series data.
method Deep convolutional architecture with a Focuser and Modeler components.
result DCIts provides interpretable coefficients and interaction patterns.
A new framework improves LSTM performance without adding more parameters.
problem Improving LSTM performance without increasing model complexity.
method A unifying framework of bilinear LSTMs that balances hidden state vector size and weight matrix approximation quality.
result Bilinear LSTMs achieve superior performance compared to linear LSTMs without additional parameters.
SEM-DNN learns reciprocal interactions from observational data without external instruments.
problem Estimating bidirectional interactions from endogenous data.
method Heteroscedastic neural simultaneous-equation estimator (SEM-DNN) that learns reciprocal structural interactions.
result SEM-DNN recovers structural effects more reliably than other methods under increasing information.
We review the geometric setting of the field theory with locally anisotropic interactions. The concept of locally anisotropic space is introduced as a general one for various type of extensions of Lagrange and Finsler geometry and higher dimension (Kaluza--Klein type) spaces. The problem of definition of spinors on gen…
CNNs reconstruct medium properties from wave probing responses.
problem Determining medium properties from wave responses.
method Deep convolutional neural networks (CNNs) for nonlinear wave equations.
result Quantitative dependence of network depth and units on medium complexity.
Novel method prioritizes genetic variants in nonlinear models.
problem Variable selection in nonlinear and nonparametric regression.
method Developed RATE measure for summarizing variable importance.
result RATE explains improved predictive accuracy of nonlinear models.
IDS integrates physics engines into deep learning for efficient, interpretable system identification.
problem Lack of generalization and interpretability in learning-based models of physical systems.
method Interactive Differentiable Simulation (IDS) that allows efficient, accurate inference of physical properties.
result Automatic task-based robot design and parameter estimation for nonlinear dynamical systems.
New framework IIA identifies innovations in general nonlinear vector autoregressive processes.
problem Limited generality of NVAR models due to additive innovation assumption.
method Independent Innovation Analysis (IIA) framework, assuming mutual independence and modulation by an auxiliary variable.
result Guarantees identifiability of innovations with arbitrary nonlinearities, up to permutation and component-wise invertible nonlinearities.
Develops a neural network for detecting nonlinear Granger causality.
problem Nonlinear interactions in neuroscience and economics.
method Multilayer perceptron with group lasso penalty for sparse estimation.
result Zero weights in input to hidden layer indicate non-causality.
Opinions and beliefs determine the evolution of social systems. This is of particular interest in finance, as the increasing complexity of financial systems is coupled with information overload. Opinion formation, therefore, is not always the result of optimal information processing. On the contrary, agents are bounded…