Search Results for author:
Found 12 papers, 7 papers with code
Contrastive inverse regression for dimension reduction
Supervised dimension reduction (SDR) has been a topic of growing interest in data science, as it enables the reduction of high-dimensional covariates while preserving the functional relation with certain response variables of interest.
Kernel Density Bayesian Inverse Reinforcement Learning
Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well.
Spherical Rotation Dimension Reduction with Geometric Loss Functions
Modern datasets often exhibit high dimensionality, yet the data reside in low-dimensional manifolds that can reveal underlying geometric structures critical for data analysis.
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior.
From the Greene--Wu Convolution to Gradient Estimation over Riemannian Manifolds
Over a complete Riemannian manifold of finite dimension, Greene and Wu introduced a convolution, known as Greene-Wu (GW) convolution.
Probabilistic Contrastive Principal Component Analysis
Recently, contrastive principal component analysis (CPCA) was proposed for this setting.
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering
In a rich variety of real data clustering applications, PEA is shown to do as well as k-means for simple datasets, while dramatically improving performance in more complex settings.
Estimating densities with nonlinear support using Fisher-Gaussian kernels
We provide theory on large support, and illustrate gains relative to competitors in simulated and real data applications.
Methodology Statistics Theory Statistics Theory
Geodesic Distance Estimation with Spherelets
When the manifold is unknown, it is challenging to accurately approximate the geodesic distance.
Efficient Weingarten Map and Curvature Estimation on Manifolds
In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space.
Classification via local manifold approximation
It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports.
Efficient Manifold and Subspace Approximations with Spherelets
There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction.
