1 citations · 2 across the 7 of their papers we have counts for
7 papers
Optimal Watermark Localization in Mixed-Source Large Language Model Texts
Jose H. Blanchet, T. Tony Cai, Xiang Li +3
Watermarking provides a principled way to authenticate text generated by large language models (LLMs). In practice, however, the final text may be mixed-source, with watermark evid…
FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
Zhiyuan Zeng, Jiashuo Liu, Siyuan Chen +28
Future prediction is a complex task for LLM agents, requiring a high level of analytical thinking, information gathering, contextual understanding, and decision-making under uncert…
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
Jiashuo Liu, Tianyu Wang, Henry Lam +2
We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulation…
Stability Evaluation via Distributional Perturbation Analysis
Jose Blanchet, Peng Cui, Jiajin Li +1
The performance of learning models often deteriorates when deployed in out-of-sample environments. To ensure reliable deployment, we propose a stability evaluation criterion based…
Automatic Outlier Rectification via Optimal Transport
Jose Blanchet, Jiajin Li, Markus Pelger +1
In this paper, we propose a novel conceptual framework to detect outliers using optimal transport with a concave cost function. Conventional outlier detection approaches typically…
Distributionally Robust Optimization and Robust Statistics
Jose Blanchet, Jiajin Li, Sirui Lin +1
We review distributionally robust optimization (DRO), a principled approach for constructing statistical estimators that hedge against the impact of deviations in the expected loss…
Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes
Xuhui Zhang, Jose Blanchet, Youssef Marzouk +2
We present the first minimax risk bounds for estimators of the spectral measure in multivariate linear factor models, where observations are linear combinations of regularly varyin…