arXiv research

Scalable algorithm for sampling Gaussian processes using sparse grids and preconditioners.

New algorithm tackles high-dimensional simulation optimization, converging efficiently.

Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficu…

SKI accelerates GP inference with sparse grids to handle higher dimensions.

Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in \cite{tang2012csotg} to recove…

Paper proposes a learning-based sparse Bayesian method for accurate off-grid DOA estimation.

We present a sparse grid high-order alternating direction implicit (ADI) scheme for option pricing in stochastic volatility models. The scheme is second-order in time and fourth-order in space. Numerical experiments confirm the computational efficiency gains achieved by the sparse grid combination technique.

This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid-based approaches, e.g., $\ell_1$ optimization and SPICE, with an infinitely dense grid…

Efficiently prices American options with multiple assets using sparse grids.

For low-dimensional data sets with a large amount of data points, standard kernel methods are usually not feasible for regression anymore. Besides simple linear models or involved heuristic deep learning models, grid-based discretizations of larger (kernel) model classes lead to algorithms, which naturally scale linear…

Deep learning improves model discovery from sparse sensor data.

SDIFT generates full-field dynamics from sparse, irregular data.

New method improves stochastic kriging for high-dimensional simulations.

Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time ener…

Sparse grids reduce xVA exposure evaluations by up to 6000 times.

In this work, we present a numerical method based on a sparse grid approximation to compute the loss distribution of the balance sheet of a financial or an insurance company. We first describe, in a stylised way, the assets and liabilities dynamics that are used for the numerical estimation of the balance sheet distrib…

Compressed sensing (CS) shows that a signal having a sparse or compressible representation can be recovered from a small set of linear measurements. In classical CS theory, the sampling matrix and representation matrix are assumed to be known exactly in advance. However, uncertainties exist due to sampling distortion, …

Method learns dynamics from sparse, irregular feature data.

New method smooths integrands for efficient option pricing.

Unified quadrature framework for large-scale kernel machines.

A new GP inference method using simplices for high-dimensional data.

The counting grid is a grid of microtopics, sparse word/feature distributions. The generative model associated with the grid does not use these microtopics individually. Rather, it groups them in overlapping rectangular windows and uses these grouped microtopics as either mixture or admixture components. This paper bui…

Study develops efficient algorithm for probabilistic penetration response of composite plates.

New SDE model for continuous-time reinforcement learning.

New learning scheme solves high-dimensional semi-linear PDEs using sparse grids and Picard approximations.

Neural Optimal Design of Experiments improves inverse problem solving efficiency.

A new approach models exploration in continuous-time RL using random measures.

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized exam…

The potential of recovering the topology of a grid using solely publicly available market data is explored here. In contemporary whole-sale electricity markets, real-time prices are typically determined by solving the network-constrained economic dispatch problem. Under a linear DC model, locational marginal prices (LM…

Estimates graph process with high-frequency data, proving asymptotic properties.

New methods solve complex financial equations.

Optimal sampling reduces power grid data analysis costs.

In high-dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received a lot of attention recently. To provide a proper amount of shrinkage, it is typical to use a grid search and a model comparison criterion to find the optimal regularization parameters. However, we show that fixing …

Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the estimated noise level. The iterat…

Extends geostatistical simulation method to handle multiple variables and large grids.

PhI-GPR improves power grid state estimation and forecasting.

Paper reviews and compares methods for handling imbalanced data.

Exact and scalable algorithm for Gaussian process regression with Matérn correlations.

Distance weighted discrimination (DWD) was originally proposed to handle the data piling issue in the support vector machine. In this paper, we consider the sparse penalized DWD for high-dimensional classification. The state-of-the-art algorithm for solving the standard DWD is based on second-order cone programming, ho…

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection…

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a…

New sampling bounds improve uniform coverage verification in machine learning.

Graph neural networks learn PDEs from sparse, irregular data.

We identify linear dynamical systems under convex constraints with fewer samples.

Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.

Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this letter, we cas…

Deep Reinforcement Learning (DRL) algorithms for continuous action spaces are known to be brittle toward hyperparameters as well as \cut{being}sample inefficient. Soft Actor Critic (SAC) proposes an off-policy deep actor critic algorithm within the maximum entropy RL framework which offers greater stability and empiric…

Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.