41 citations · 44 across the 5 of their papers we have counts for
5 papers
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…
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…
Talk Me Through It: Developing Effective Systems for Chart Authoring
Nazar Ponochevnyi, Young-Ho Kim, Joseph Jay Williams +1
Recent chart-authoring systems increasingly focus on natural-language input, enabling users to form a mental image of the chart they wish to create and express this intent using sp…
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…
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content
Avi Segal, Yossi Ben David, Joseph Jay Williams +2
As e-learning systems become more prevalent, there is a growing need for them to accommodate individual differences between students. This paper addresses the problem of how to per…