Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Zhu, Henry"'
Randomized controlled trials are often run in settings with many subpopulations that may have differential benefits from the treatment being evaluated. We consider the problem of sample selection, i.e., whom to enroll in a randomized trial, such as t
Externí odkaz:
http://arxiv.org/abs/2403.01386
Autor:
Wang, Sijia, Li, Alexander Hanbo, Zhu, Henry, Zhang, Sheng, Hang, Chung-Wei, Perera, Pramuditha, Ma, Jie, Wang, William, Wang, Zhiguo, Castelli, Vittorio, Xiang, Bing, Ng, Patrick
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables. While existing entity linking (EL) models work well on per modality configuration, such as text-only EL, visual grounding, or schema linki
Externí odkaz:
http://arxiv.org/abs/2305.17337
Autor:
Ribeiro, Danilo, Wang, Shen, Ma, Xiaofei, Zhu, Henry, Dong, Rui, Kong, Deguang, Burger, Juliette, Ramos, Anjelica, Wang, William, Huang, Zhiheng, Karypis, George, Xiang, Bing, Roth, Dan
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step struc
Externí odkaz:
http://arxiv.org/abs/2302.06729
Autor:
Zhao, Yiyun, Jiang, Jiarong, Hu, Yiqun, Lan, Wuwei, Zhu, Henry, Chauhan, Anuj, Li, Alexander, Pan, Lin, Wang, Jun, Hang, Chung-Wei, Zhang, Sheng, Dong, Marvin, Lilien, Joe, Ng, Patrick, Wang, Zhiguo, Castelli, Vittorio, Xiang, Bing
Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further
Externí odkaz:
http://arxiv.org/abs/2212.08785
Autor:
Ribeiro, Danilo, Wang, Shen, Ma, Xiaofei, Dong, Rui, Wei, Xiaokai, Zhu, Henry, Chen, Xinchi, Huang, Zhiheng, Xu, Peng, Arnold, Andrew, Roth, Dan
Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain
Externí odkaz:
http://arxiv.org/abs/2205.09224
In an attempt to make algorithms fair, the machine learning literature has largely focused on equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate, consider a hypothetical government rideshare program that provid
Externí odkaz:
http://arxiv.org/abs/2109.08792
Autor:
Shakeri, Siamak, Santos, Cicero Nogueira dos, Zhu, Henry, Ng, Patrick, Nan, Feng, Wang, Zhiguo, Nallapati, Ramesh, Xiang, Bing
We propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions. In a nutshell, we feed a passage to the enco
Externí odkaz:
http://arxiv.org/abs/2010.06028
Autor:
Zhu, Henry, Yu, Justin, Gupta, Abhishek, Shah, Dhruv, Hartikainen, Kristian, Singh, Avi, Kumar, Vikash, Levine, Sergey
The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversight to enable continuous learning. In this work, we discuss the element
Externí odkaz:
http://arxiv.org/abs/2004.12570
Autor:
Ahn, Michael, Zhu, Henry, Hartikainen, Kristian, Ponte, Hugo, Gupta, Abhishek, Levine, Sergey, Kumar, Vikash
Publikováno v:
Conference on Robot Learning, 2019
ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-finger
Externí odkaz:
http://arxiv.org/abs/1909.11639
Autor:
Haarnoja, Tuomas, Zhou, Aurick, Hartikainen, Kristian, Tucker, George, Ha, Sehoon, Tan, Jie, Kumar, Vikash, Zhu, Henry, Gupta, Abhishek, Abbeel, Pieter, Levine, Sergey
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods typically suffer from two major challenges: high sample complexity an
Externí odkaz:
http://arxiv.org/abs/1812.05905