25 citations · 104 across the 13 of their papers we have counts for
14 papers
Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses
Lulu Kang, Xinwei Deng, Ran Jin
Systems with both quantitative and qualitative responses are widely encountered in many applications. Design of experiment methods are needed when experiments are conducted to stud…
Sampling Constrained Continuous Probability Distributions: A Review
Shiwei Lan, Lulu Kang
The problem of sampling constrained continuous distributions has frequently appeared in many machine/statistical learning models. Many Monte Carlo Markov Chain (MCMC) sampling meth…
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
Yindong Chen, Yiwei Wang, Lulu Kang +1
We propose a novel deterministic sampling method, EVI-MMD, to approximate a target distribution by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepan…
A Generative Approach to Joint Modeling of Quantitative and Qualitative Responses
Xiaoning Kang, Lulu Kang, Wei Chen +1
In many scientific areas, data with quantitative and qualitative (QQ) responses are commonly encountered with a large number of predictors. By exploring the association between QQ…
Fair and Diverse Allocation of Scarce Resources
Hadis Anahideh, Lulu Kang, Nazanin Nezami
We aim to design a fairness-aware allocation approach to maximize the geographical diversity and avoid unfairness in the sense of demographic disparity. During the development of t…
Bayesian Auxiliary Variable Model for Birth Records Data with Qualitative and Quantitative Responses
Xiaoning Kang, Shyam Ranganathan, Lulu Kang +2
Many applications involve data with qualitative and quantitative responses. When there is an association between the two responses, a joint model will provide improved results than…
A Maximin -Efficient Design for Multivariate GLM
Yiou Li, Lulu Kang, Xinwei Deng
Experimental designs for a generalized linear model (GLM) often depend on the specification of the model, including the link function, the predictors, and unknown parameters, such…
Locally Optimal Design for A/B Testing in the Presence of Covariates and Network Connection
Qiong Zhang, Lulu Kang
A/B test, a simple type of controlled experiment, refers to the statistical procedure of experimenting to compare two treatments applied to test subjects. For example, many IT comp…
Covariate Balancing Based on Kernel Density Estimates for Controlled Experiments
Yiou Li, Lulu Kang, Xiao Huang
Controlled experiments are widely used in many applications to investigate the causal relationship between input factors and experimental outcomes. A completely randomized design i…
Is a Transformed Low Discrepancy Design Also Low Discrepancy?
Yiou Li, Lulu Kang, Fred J. Hickernell
Experimental designs intended to match arbitrary target distributions are typically constructed via a variable transformation of a uniform experimental design. The inverse distribu…
Particle-based Energetic Variational Inference
Yiwei Wang, Jiuhai Chen, Chun Liu +1
We introduce a new variational inference (VI) framework, called energetic variational inference (EVI). It minimizes the VI objective function based on a prescribed energy-dissipati…
Gaussian Process Assisted Active Learning of Physical Laws
Jiuhai Chen, Lulu Kang, Guang Lin
In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in…
D-optimal Design for Network A/B Testing
Victoria Pokhiko, Qiong Zhang, Lulu Kang +1
A/B testing refers to the statistical procedure of conducting an experiment to compare two treatments, A and B, applied to different testing subjects. It is widely used by technolo…
Kernel Discrepancy-Based Rerandomization for Controlled Experiments
Yiou Li, Lulu Kang
This paper introduces a kernel discrepancy-based framework for rerandomization to enhance the precision of causal inference in controlled experiments. We demonstrate that the kerne…