5 papers
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni +3
We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the leader observes contextual infor…
Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas
Luke Guerdan, Justin Whitehouse, Kimberly Truong +2
As Generative AI (GenAI) systems see growing adoption, a key concern involves the external validity of evaluations, or the extent to which they generalize from lab-based to real-wo…
Time-Uniform Self-Normalized Concentration for Vector-Valued Processes
Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
Self-normalized processes arise naturally in many learning-related tasks. While self-normalized concentration has been extensively studied for scalar-valued processes, there are fe…
Orthogonal Causal Calibration
Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis +2
Estimates of heterogeneous treatment effects such as conditional average treatment effects (CATEs) and conditional quantile treatment effects (CQTEs) play an important role in real…
Regret Minimization in Stackelberg Games with Side Information
Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
Algorithms for playing in Stackelberg games have been deployed in real-world domains including airport security, anti-poaching efforts, and cyber-crime prevention. However, these a…