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.
DeepICMGP surrogate models multiple outputs efficiently.
E-LMC improves spatial field prediction accuracy by linearizing complex fields.
Develops a scalable multi-task Gaussian process with neural embedding for improved performance.
MO-GP models fill gaps in biophysical data with across-domain info transfer.
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent latent functions, all current MTGPs including the salient linear model of coregionalization (LMC) and convolution frameworks cannot effectively…
The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generat…
IPGP framework improves psychological assessment by integrating shared and unique traits.
Paper introduces a new model to handle multi-task learning across different input domains.
Despite the effectiveness of multitask deep neural network (MTDNN), there is a limited theoretical understanding on how the information is shared across different tasks in MTDNN. In this work, we establish a formal connection between MTDNN with infinitely-wide hidden layers and multitask Gaussian Process (GP). We deriv…
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are als…
CMDE uses deep learning to estimate causal effects from complex data.
The paper tackles counterfactual inference with multioutput deep kernels in high-dimensional settings.
Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for …
A scalable MOGP model with stochastic variational inference for many outputs.
Multi-output Gaussian processes have received increasing attention during the last few years as a natural mechanism to extend the powerful flexibility of Gaussian processes to the setup of multiple output variables. The key point here is the ability to design kernel functions that allow exploiting the correlations betw…
In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their statu…
Detecting anomalies in multivariate functional data using Bayesian nonparametric methods.
Efficiently predicts high-fidelity PDE solutions using multi-fidelity Gaussian processes.
Extends GP regression to complex Helmholtz problems, improving wavefield inference in brain elastography.