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Inverse Contextual Bandits without Rewards: Learning from a Non-Stationary Learner via Suffix Imitation
Yuqi Kong, Xiao Zhang, Weiran Shen
We study the Inverse Contextual Bandit (ICB) problem, in which a learner seeks to optimize a policy while an observer, who cannot access the learner's rewards and only observes act…
Calibration without Ground Truth
Yuqing Kong, Mingyu Song, Yizhou Wang +1
Villalobos et al. [2024] predict that publicly available human text will be exhausted within the next decade. Thus, improving models without access to ground-truth labels becomes i…
Mitigating the Participation Bias by Balancing Extreme Ratings
Yongkang Guo, Yuqing Kong, Jialiang Liu
Rating aggregation plays a crucial role in various fields, such as product recommendations, hotel rankings, and teaching evaluations. However, traditional averaging methods can be…
The Surprising Benefits of Base Rate Neglect in Robust Aggregation
Yuqing Kong, Shu Wang, Ying Wang
Robust aggregation integrates predictions from multiple experts without knowledge of the experts' information structures. Prior work assumes experts are Bayesian, providing predict…
Robust Decision Aggregation with Adversarial Experts
Yongkang Guo, Yuqing Kong
We consider a robust aggregation problem in the presence of both truthful and adversarial experts. The truthful experts will report their private signals truthfully, while the adve…
Algorithmic Robust Forecast Aggregation
Yongkang Guo, Jason D. Hartline, Zhihuan Huang +3
Forecast aggregation combines the predictions of multiple forecasters to improve accuracy. However, the lack of knowledge about forecasters' information structure hinders optimal a…