2 citations · 2 across the 5 of their papers we have counts for
5 papers
Estimating the True Effect Size Distribution with SIMEX
Zhaoqi Li, Daniel Ting, Ilya Gorbachev +2
Large-scale online experimentation produces noisy effect estimates, which can overstate gains and complicate decisions about launches and testing policies. We propose a nonparametr…
A Statistical Test for the Benefits of Personalizing Interventions
Zhaoqi Li, Emma Brunskill
From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's…
Active Learning for Stochastic Contextual Linear Bandits
Emma Brunskill, Ishani Karmarkar, Zhaoqi Li
A key goal in stochastic contextual linear bandits is to efficiently learn a near-optimal policy. Prior algorithms for this problem learn a policy by strategically sampling actions…
Estimation of subsidiary performance metrics under optimal policies
Zhaoqi Li, Houssam Nassif, Alex Luedtke
In policy learning, the goal is typically to optimize a primary performance metric, but other subsidiary metrics often also warrant attention. This paper presents two strategies fo…
Optimal Exploration is no harder than Thompson Sampling
Zhaoqi Li, Kevin Jamieson, Lalit Jain
Given a set of arms and an unknown parameter vector , the pure exploration linear bandit problem aims to return $\arg\max_{…