arXiv research

The paper introduces moment multicalibration for estimating uncertainty across subgroups.

New method unfolds distribution moments directly from data without binning.

For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time hori…

This paper uses HCR to predict bid-ask spreads from accessible data.

The paper sets limits on the accuracy of macroeconomic forecasts based on statistical moments and trade volumes.

The paper explores how market-based returns depend on past trade values.

A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.

Paper identifies tensor ranks via prior predictive matching, solving system of equations.

Dynamic Boltzmann Machine (DyBM) has been shown highly efficient to predict time-series data. Gaussian DyBM is a DyBM that assumes the predicted data is generated by a Gaussian distribution whose first-order moment (mean) dynamically changes over time but its second-order moment (variance) is fixed. However, in many fi…

Model predicts epileptic seizures with high accuracy using EEG signals.

New method uses machine learning to improve statistical inference.

TGNN combines GNN and SMM for better trading network predictions.

Machine learning models accurately predict molecular magnetic anisotropy tensors.

The paper examines how market trade values and volumes affect price autocorrelation.

Market-based asset price probability depends on trade volumes and values, improving forecasts and reliability.

Price and return predictions are limited by economic complexity, not just volatility.

MuML models predict molecular dipole moments using atomic partial charges and dipoles.

CatBoostLSS predicts entire conditional distributions for probabilistic forecasting.

Proposes a method to use external machine-learning predictions in multinomial logistic regression.

Develops a multilevel Monte Carlo framework with dropout for efficient uncertainty quantification.

A new framework for lightweight BNNs learns heteroscedastic uncertainties efficiently.

Factorial moments are convenient tools in particle physics to characterize the multiplicity distributions when phase-space resolution ($Δ$) becomes small. They include all correlations within the system of particles and represent integral characteristics of any correlation between these particles. In this letter, we sh…

We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables. Our algorithm assumes that every latent variable has an "anchor", an observed variable with only that latent variable as its parent. Given such anchors, we show that it is possi…

New algorithm for risk-sensitive reinforcement learning with natural policy gradients.

Paper explores ML for UV spectra, showing transferability in chemical space.

New estimator improves statistical validity of synthetic data integration.

Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learnin…

Improved neural network models predict molecular and material properties efficiently.

Study on quadratic L-functions using hyperelliptic curves and homology.

Rotationally equivariant convolutions improve molecular property prediction.

This paper deals with the explicit design of strategy formulations to make the best strategic choices from a conventional matrix form of representing strategic choices. The explicit strategy formulation is an analytical model which is targeted to provide a mathematical strategy framework to find the best moment for str…

The paper proposes a method to monitor deep learning predictions for retraining, reducing costs.

Synthesizes robust estimators for domain adaptation.

Framework predicts nonlinear system responses using GFDT and generative models.

In this paper, we study the confounder detection problem in the linear model, where the target variable $Y$ is predicted using its $n$ potential causes $X_n=(x_1,...,x_n)^T$. Based on an assumption of rotation invariant generating process of the model, recent study shows that the spectral measure induced by the regress…

Deep neural networks predict earthquake locations with high accuracy.

New framework quantifies uncertainty in data and models using RKHS.

A2-SBNN models spatial data with copulas for non-Gaussian dependencies.

Develops a deterministic method to approximate NSDEs for better uncertainty quantification.

Study evaluates interpretability of time series foundation models' latent spaces.

Bayesian framework uses AI-generated data to improve parameter estimation.

ELSA efficiently adapts to label shift without post-prediction calibrations.

This paper clarifies Bitcoin's volatility and predictability across daily, weekly, and monthly scales.

Computing expected predictions of discriminative models is a fundamental task in machine learning that appears in many interesting applications such as fairness, handling missing values, and data analysis. Unfortunately, computing expectations of a discriminative model with respect to a probability distribution defined…

We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the $n$ stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each tradin…

A new method calculates fractional moments using the moment-generating function.

Deep learning predicts stock movements using social media data.

Study compares weak and homotopy moment maps in multisymplectic geometry.