10 papers
Sample Complexity of Multicalibration for Multilevel Properties
Jiuyao Lu, Krishnakumar Balasubramanian, Aleksandr Podkopaev +1
Calibration requires a predictor to be unbiased after conditioning on its own predictions. Multicalibration asks for this guarantee simultaneously across a collection of groups. Ma…
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
Xiaomin Li, Yuexing Hao, Jianheng Hou +90
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…
The Sample Complexity of Multicalibration
Natalie Collina, Jiuyao Lu, Georgy Noarov +1
We study the minimax sample complexity of multicalibration in the batch setting. A learner observes i.i.d. samples from an unknown distribution and must output a (possibly rand…
Fast Rerandomization for Balancing Covariates in Randomized Experiments: A Metropolis-Hastings Framework
Jiuyao Lu, Tianruo Zhang, Ke Zhu
Balancing covariates is critical for credible and efficient randomized experiments. Rerandomization addresses this by repeatedly generating treatment assignments until covariate ba…
Optimal Lower Bounds for Online Multicalibration
Natalie Collina, Jiuyao Lu, Georgy Noarov +1
We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions ca…
Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
Yahav Bechavod, Jiuyao Lu, Aaron Roth
We present an algorithm guaranteeing dynamic regret bounds for online omniprediction with long term constraints. The goal in this recently introduced problem is for a learner to ge…
Online Omniprediction with Long-Term Constraints
Yahav Bechavod, Jiuyao Lu, Aaron Roth
We introduce and study the problem of online omniprediction with long-term constraints. At each round, a forecaster is tasked with generating predictions for an underlying (adaptiv…
Causal Feature Learning in the Social Sciences
Jingzhou Huang, Jiuyao Lu, Alexander Williams Tolbert
Variable selection poses a significant challenge in causal modeling, particularly within the social sciences, where constructs often rely on inter-related factors such as age, soci…
Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses
Jiuyao Lu, Aaron Roth, Mirah Shi
We define "decision swap regret" which generalizes both prediction for downstream swap regret and omniprediction, and give algorithms for obtaining it for arbitrary multi-dimension…
Fast Rerandomization via the BRAIN
Jiuyao Lu, Daogao Liu, Zhanran Lin +1
Randomized experiments are a crucial tool for causal inference in many different fields. Rerandomization addresses any covariate imbalance in such experiments by resampling treatme…