3 citations · 3 across the 2 of their papers we have counts for
7 papers
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization
Tong Li, Jacob Nogas, Haochen Song +8
Traditional randomized A/B experiments assign arms with uniform random (UR) probability, such as 50/50 assignment to two versions of a website to discover whether one version engag…
RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits
Tong Li, Thiago de Queiroz Casanova, Eric M. Schwartz +3
Real-world contextual bandit problems with complex reward models are often tackled with iteratively trained models, such as boosting trees. However, it is difficult to directly app…
Modeling Cell Developmental Trajectory using Multinomial Unbalanced Optimal Transport
Junhao Zhu, Kevin Zhang, Zhaolei Zhang +1
Single-cell trajectory analysis aims to reconstruct the biological developmental processes of cells as they evolve over time, leveraging temporal correlations in gene expression. D…
A Statistically Reliable Optimization Framework for Bandit Experiments in Scientific Discovery
Tong Li, Travis Mandel, Goldie Phillips +4
Scientific experimentation is largely driven by statistical hypothesis testing to determine significant differences in interventions. Traditionally, experimenters allocate samples…
Successive classification learning for estimating quantile optimal treatment regimes
Junwen Xia, Jingxiao Zhang, Dehan Kong
Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients' outcomes. Compared to treatment regimes that target the mean outc…
Convergence and Optimality of the EM Algorithm Under Multi-Component Gaussian Mixture Models
Xin Bing, Dehan Kong, Bingqing Li
Gaussian mixture models (GMMs) are fundamental statistical tools for modeling heterogeneous data. Due to the nonconcavity of the likelihood function, the Expectation-Maximization (…