arXiv research

Probabilistic grammars improve equation discovery from data.

Bayesian method reconstructs hidden higher-order interactions from network data.

The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametr…

PASTIS selects minimal models from stochastic dynamics data.

Complexity helps identify sparse risk factors in asset pricing.

Parsimonious neural networks discover interpretable physical laws from data.

In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization…

We propose a graph spectral representation of time series data that 1) is parsimoniously encoded to user-demanded resolution; 2) is unsupervised and performant in data-constrained scenarios; 3) captures event and event-transition structure within the time series; and 4) has near-linear computational complexity in both …

PASTIS method selects simple models from noisy data.

Data science principles enhance AI interpretability for better user control.

We propose a parsimonious topic model for text corpora. In related models such as Latent Dirichlet Allocation (LDA), all words are modeled topic-specifically, even though many words occur with similar frequencies across different topics. Our modeling determines salient words for each topic, which have topic-specific pr…

This paper proposes a parsimoniously time varying parameter vector autoregressive model (with exogenous variables, VARX) and studies the properties of the Lasso and adaptive Lasso as estimators of this model. The parameters of the model are assumed to follow parsimonious random walks, where parsimony stems from the ass…

A parsimonious model reduces over-parameterization in skewed matrix variate mixtures.

A family of parsimonious shifted asymmetric Laplace mixture models is introduced. We extend the mixture of factor analyzers model to the shifted asymmetric Laplace distribution. Imposing constraints on the constitute parts of the resulting decomposed component scale matrices leads to a family of parsimonious models. An…

Koopman Regularization learns governing equations from sparse data.

Bayesian inference simplified for machine learning models.

A family of parsimonious Gaussian cluster-weighted models is presented. This family concerns a multivariate extension to cluster-weighted modelling that can account for correlations between multivariate responses. Parsimony is attained by constraining parts of an eigen-decomposition imposed on the component covariance …

New risk metric for AI systems reduces safety risks with minimal data.

MDL principle aids in learning neural network-based causal structures.

We propose an elementary model to price European physical delivery swaptions in multicurve setting with a simple exact closed formula. The proposed model is very parsimonious: it is a three-parameter multicurve extension of the two-parameter Hull-White (1990) model. The model allows also to obtain simple formulas for a…

This paper extends exponential smoothing to distributional time series using Wasserstein distance.

A new method solves complex hydroelectricity planning problems.

Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate …

New GMM models fit high-dimensional data with fewer parameters.

Inferring a decision tree from a given dataset is one of the classic problems in machine learning. This problem consists of buildings, from a labelled dataset, a tree such that each node corresponds to a class and a path between the tree root and a leaf corresponds to a conjunction of features to be satisfied in this c…

Develops a Bayesian framework for symbolic regression of scientific expressions.

We consider the problem of non-parametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well-suited for high-dimensional sparse additive models. The proposed approach combines appealing features of finite basis representation and smoot…

Investigates deep hedging under rough volatility models.

Bayesian context trees capture complex dependencies in categorical sequences.

Proposes a non-convex optimization method for a parsimonious weighted naive Bayes classifier.

New neural networks model complex phenomena with fewer parameters.

Introduces LLC, a new complexity measure for DNNs based on SLT.

Develops framework for valuing and assessing risk of renewable PPAs.

A new method reduces high-dimensional data's impact on CWMs using TSNE.

Method selects most useful network model for various tasks.

We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length (MDL) principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obta…

Let $G$ be a nonabelian, simple group with a nontrivial conjugacy class $C \subseteq G$. Let $K$ be a diagram of an oriented knot in $S^3$, thought of as computational input. We show that for each such $G$ and $C$, the problem of counting homomorphisms $π_1(S^3\setminus K) \to G$ that send meridians of $K$ to $C$ is al…

Parsimonious Dynamic Mode Decomposition selects sparse modes robustly.

Proposes a new approach to approximate maximum likelihood for complex models.

Optimal AFs minimize RFR test error and sensitivity.

Novel estimation methods improve MAR model accuracy for high-dimensional time series.

Path regularization reveals convex optimization in deep ReLU networks.

Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a PB…

Recent years have demonstrated that using random feature maps can significantly decrease the training and testing times of kernel-based algorithms without significantly lowering their accuracy. Regrettably, because random features are target-agnostic, typically thousands of such features are necessary to achieve accept…

Researchers develop multi-utility representations for incomplete preferences linked to risk measures.

For a long time interest-rate models were built on a single yield curve used both for discounting and forwarding. However, the crisis that has affected financial markets in the last years led market players to revise this assumption and accommodate basis-swap spreads, whose remarkable widening can no longer be neglecte…

AdaRL adapts quickly to new environments with minimal data.

Discovering and characterizing the large-scale topological features in empirical networks are crucial steps in understanding how complex systems function. However, most existing methods used to obtain the modular structure of networks suffer from serious problems, such as being oblivious to the statistical evidence sup…