no code implementations • 20 May 2023 • Sam Hawke, Hengrui Luo, Didong Li
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.
1 code implementation • 13 Mar 2023 • Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt
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.
1 code implementation • 23 Apr 2022 • Hengrui Luo, Jeremy E. Purvis, Didong Li
Modern datasets often exhibit high dimensionality, yet the data reside in low-dimensional manifolds that can reveal underlying geometric structures critical for data analysis.
no code implementations • pproximateinference AABI Symposium 2022 • Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara Engelhardt
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior.
no code implementations • 17 Aug 2021 • Tianyu Wang, Yifeng Huang, Didong Li
Over a complete Riemannian manifold of finite dimension, Greene and Wu introduced a convolution, known as Greene-Wu (GW) convolution.
2 code implementations • 14 Dec 2020 • Didong Li, Andrew Jones, Barbara Engelhardt
Recently, contrastive principal component analysis (CPCA) was proposed for this setting.
no code implementations • 17 Aug 2020 • Debolina Paul, Saptarshi Chakraborty, Didong Li, David Dunson
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.
1 code implementation • 12 Jul 2019 • Minerva Mukhopadhyay, Didong Li, David B Dunson
We provide theory on large support, and illustrate gains relative to competitors in simulated and real data applications.
Methodology Statistics Theory Statistics Theory
no code implementations • 29 Jun 2019 • Didong Li, David B. Dunson
When the manifold is unknown, it is challenging to accurately approximate the geodesic distance.
1 code implementation • 26 May 2019 • Yueqi Cao, Didong Li, Huafei Sun, Amir H Assadi, Shiqiang Zhang
In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space.
1 code implementation • 3 Mar 2019 • Didong Li, David B. Dunson
It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports.
3 code implementations • 26 Jun 2017 • Didong Li, Minerva Mukhopadhyay, David B. Dunson
There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction.