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Found 13 papers, 1 papers with code
Targeted Variance Reduction: Robust Bayesian Optimization of Black-Box Simulators with Noise Parameters
In such applications, the simulator often takes the form $f(\mathbf{x},\boldsymbol{\theta})$, where $\boldsymbol{\theta}$ are parameters that are uncertain in practice.
$e^{\text{RPCA}}$: Robust Principal Component Analysis for Exponential Family Distributions
Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers.
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression
Fourier feature approximations have been successfully applied in the literature for scalable Gaussian Process (GP) regression.
Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma
The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang.
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
A widely-used emulator is the Gaussian process (GP), which provides a flexible framework for efficient prediction and uncertainty quantification.
PERCEPT: a new online change-point detection method using topological data analysis
This yields a non-parametric, topology-aware framework which can efficiently detect online changes from high-dimensional data streams.
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales
Algorithmic harmonization - the automated harmonization of a musical piece given its melodic line - is a challenging problem that has garnered much interest from both music theorists and computer scientists.
Gaussian Process Subspace Regression for Model Reduction
In PROM, each parameter point can be associated with a subspace, which is used for Petrov-Galerkin projections of large system matrices.
Sequential change-point detection for mutually exciting point processes over networks
We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a. k. a.
TSEC: a framework for online experimentation under experimental constraints
We propose a new Thompson Sampling under Experimental Constraints (TSEC) method, which addresses this so-called "arm budget constraint".
Bayesian Uncertainty Quantification for Low-Rank Matrix Completion
We consider the problem of uncertainty quantification for an unknown low-rank matrix $\mathbf{X}$, given a partial and noisy observation of its entries.
Decision Making
Low-Rank Matrix Completion
Methodology
Uncertainty Quantification for Inferring Hawkes Networks
Multivariate Hawkes processes are commonly used to model streaming networked event data in a wide variety of applications.
Distributional Clustering: A distribution-preserving clustering method
One key use of k-means clustering is to identify cluster prototypes which can serve as representative points for a dataset.
