Pearl: A Production-ready Reinforcement Learning Agent

1 code implementation • 6 Dec 2023 • Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu

Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.

reinforcement-learning Reinforcement Learning (RL)

On Noisy Evaluation in Federated Hyperparameter Tuning

1 code implementation • 17 Dec 2022 • Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith

In this work, we perform the first systematic study on the effect of noisy evaluation in federated hyperparameter tuning.

Federated Learning

Interpretable Personalized Experimentation

no code implementations • 5 Nov 2021 • Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, Eytan Bakshy

Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.

Extreme Multi-label Learning for Semantic Matching in Product Search

1 code implementation • 23 Jun 2021 • Wei-Cheng Chang, Daniel Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon

In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.

Extreme Multi-Label Classification Multi-Label Learning