1 citations · 1 across the 6 of their papers we have counts for
6 papers
Learning bounds for doubly-robust covariate shift adaptation
Jeonghwan Lee, Cong Ma
Distribution shift between the training domain and the test domain poses a key challenge for modern machine learning. An extensively studied instance is the \emph{covariate shift},…
The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
Rong Jiang, Cong Ma
We study batched nonparametric contextual bandits under a margin condition when the margin parameter is unknown. To capture the statistical cost of this ignorance, we introduce…
ShoppingComp: Are LLMs Really Ready for Your Shopping Cart?
Huaixiao Tou, Ying Zeng, Yuemeng Li +6
We present ShoppingComp, a challenging real-world benchmark for comprehensively evaluating LLM-powered shopping agents on three core capabilities: precise product retrieval, expert…
Auditing Differential Privacy in the Black-Box Setting
Kaining Shi, Cong Ma
This paper introduces a novel theoretical framework for auditing differential privacy (DP) in a black-box setting. Leveraging the concept of -differential privacy, we explicitly…
Batched Nonparametric Contextual Bandits
Rong Jiang, Cong Ma
We study nonparametric contextual bandits under batch constraints, where the expected reward for each action is modeled as a smooth function of covariates, and the policy updates a…
On the design-dependent suboptimality of the Lasso
Reese Pathak, Cong Ma
This paper investigates the effect of the design matrix on the ability (or inability) to estimate a sparse parameter in linear regression. More specifically, we characterize the op…