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.
NPOD algorithm improves efficiency in estimating pharmacokinetic parameters.
SketchGraphs dataset aids in modeling CAD designs.
This work introduces benchmarks for evaluating nanophotonic structures in design simulations.
GBS uses machine learning to design products based on consumer preferences.
This work introduces the concept of parametric Gaussian processes (PGPs), which is built upon the seemingly self-contradictory idea of making Gaussian processes parametric. Parametric Gaussian processes, by construction, are designed to operate in "big data" regimes where one is interested in quantifying the uncertaint…
We consider a class of misspecified dynamical models where the governing term is only approximately known. Under the assumption that observations of the system's evolution are accessible for various initial conditions, our goal is to infer a non-parametric correction to the misspecified driving term such as to faithful…
New ADANNs improve PDE approximations.
Parametric insurance offers better risk-sharing in high-risk settings than traditional indemnity insurance.
Proof of learning rate transfer in MLPs with P parameterization.
We propose an input design method for a general class of parametric probabilistic models, including nonlinear dynamical systems with process noise. The goal of the procedure is to select inputs such that the parameter posterior distribution concentrates about the true value of the parameters; however, exact computation…
New method minimizes robust density power-based divergences for general parametric densities.
This paper won 1st place in forecasting and investment challenges, improving on meta-learning and parametric models.
Simpler GNNs with low-rank non-parametric aggregators perform well on graph benchmarks.
We introduce a novel approach to perform first-order optimization with orthogonal and unitary constraints. This approach is based on a parametrization stemming from Lie group theory through the exponential map. The parametrization transforms the constrained optimization problem into an unconstrained one over a Euclidea…
A model-based optimal experiment design (OED) of nonlinear systems is studied. OED represents a methodology for optimizing the geometry of the parametric joint-confidence regions (CRs), which are obtained in an a posteriori analysis of the least-squares parameter estimates. The optimal design is achieved by using the a…
Paper introduces a new power-dominance axis in estimator design.
Physical modeling of robotic system behavior is the foundation for controlling many robotic mechanisms to a satisfactory degree. Mechanisms are also typically designed in a way that good model accuracy can be achieved with relatively simple models and model identification strategies. If the modeling accuracy using phys…
In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes. Unlike parametric estimation, where evolutionary dynamics can be exploited for fast computation of the gradient, and unlike typical function learning, where representer theorem is readily …
This work presents a methodology to design trajectory tracking feedback control laws, which embed non-parametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysis …
The paper analyzes high-dimensional linear regression using parametric empirical Bayes methods.
Kernelised flows improve density estimation and generation with fewer parameters.
Estimates non-parametric logistic model using case-control data and external summary info.
New method optimizes multiple objectives in A/B testing for AI and clinical trials.
The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some chall…
Framework improves data-driven ROMs for complex systems using Bayesian operator inference.
Study motion planning for points avoiding obstacles in a plane.
A nonparametric two-sample test using a parametric integral probability metric
New learning rule simplifies Bayesian updates for deep learning.
A practical limitation of deep neural networks is their high degree of specialization to a single task and visual domain. Recently, inspired by the successes of transfer learning, several authors have proposed to learn instead universal, fixed feature extractors that, used as the first stage of any deep network, work w…
Gradient-free framework for Bayesian experimental design in complex systems.
New algorithm reduces dynamic regret for noisy gradient feedback with piecewise polynomial comparators.
Unified method visualizes curvature on curves and surfaces.
Machine learning finds a compact fixed point action for SU(3) gauge theory.
Bézier-GAN optimizes airfoil design by reducing shape complexity.
New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.
KQT-EWMA monitors multivariate data streams online with flexible and practical change detection.
Develops a new model for network estimation from multi-variate data.
We consider solution of stochastic storage problems through regression Monte Carlo (RMC) methods. Taking a statistical learning perspective, we develop the dynamic emulation algorithm (DEA) that unifies the different existing approaches in a single modular template. We then investigate the two central aspects of regres…
We propose a novel approach for density estimation with exponential families for the case when the true density may not fall within the chosen family. Our approach augments the sufficient statistics with features designed to accumulate probability mass in the neighborhood of the observed points, resulting in a non-para…
MCD automates counterfactual design searches for multi-modal tasks.
Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can no…
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, withou…
Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the source of the problem that produced the anomaly is also essential. This is particularly the case in aircraft engine health monitoring where detecting early signs of failure (anomalies) and helping the engine …
Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear combinations of independent, latent, single-output Gaussian processes (GPs). This resulted in cross-covariance functions with limited parametric interpretation, thus conflicting with the ability of single-output GPs to understand lengthscal…
Paper relaxes assumptions for non-parametric estimation in pairwise learning.
We introduce a method to design lightweight shell objects that are structurally robust under the external forces they may experience during use. Given an input 3D model and a general description of the external forces, our algorithm generates a structurally-sound minimum weight shell object. Our approach works by alter…
The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models …
Optimizes predictions for specific tasks using parametrized decision analysis.