Search Results for author:
Found 14 papers, 6 papers with code
Meritocracy and Its Discontents: Long-run Effects of Repeated School Admission Reforms
What happens if selective colleges change their admission policies?
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
We show that the resulting estimator, which we call Adaptive IPS (AIPS), can be unbiased under any complex user behavior.
Counterfactual Learning with General Data-generating Policies
Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy.
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as the evaluation policy, number of actions, and noise level.
Approximating Choice Data by Discrete Choice Models
We obtain a necessary and sufficient condition under which random-coefficient discrete choice models, such as mixed-logit models, are rich enough to approximate any nonparametric random utility models arbitrarily well across choice sets.
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions.
Evaluating the Robustness of Off-Policy Evaluation
Unfortunately, identifying a reliable estimator from results reported in research papers is often difficult because the current experimental procedure evaluates and compares the estimators' performance on a narrow set of hyperparameters and evaluation policies.
Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
Algorithms make a growing portion of policy and business decisions.
Curse of Democracy: Evidence from the 21st Century
Democracy is widely believed to contribute to economic growth and public health in the 20th and earlier centuries.
Breaking Ties: Regression Discontinuity Design Meets Market Design
The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at ``screened'' schools, combined with lottery tie-breaking at unscreened ``lottery'' schools.
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Our dataset is unique in that it contains a set of multiple logged bandit datasets collected by running different policies on the same platform.
Debiased Off-Policy Evaluation for Recommendation Systems
Efficient methods to evaluate new algorithms are critical for improving interactive bandit and reinforcement learning systems such as recommendation systems.
Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
In adaptive experimental design, the experimenter is allowed to change the probability of assigning a treatment using past observations for estimating the ATE efficiently.
Efficient Counterfactual Learning from Bandit Feedback
What is the most statistically efficient way to do off-policy evaluation and optimization with batch data from bandit feedback?
Ranked #1 on
Visual Object Tracking
on VOT2014
