2k citations · 2k across the 8 of their papers we have counts for
8 papers
DSMentor: Enhancing Data Science Agents with Curriculum Learning and Online Knowledge Accumulation
He Wang, Alexander Hanbo Li, Yiqun Hu +6
Large language model (LLM) agents have shown promising performance in generating code for solving complex data science problems. Recent studies primarily focus on enhancing in-cont…
Minimax-Regret Sample Selection in Randomized Experiments
Yuchen Hu, Henry Zhu, Emma Brunskill +1
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 s…
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Danilo Ribeiro, Shen Wang, Xiaofei Ma +8
Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations,…
Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making
Alex Chohlas-Wood, Madison Coots, Henry Zhu +2
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 illust…
The Ingredients of Real-World Robotic Reinforcement Learning
Henry Zhu, Justin Yu, Abhishek Gupta +5
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 oversig…
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
Michael Ahn, Henry Zhu, Kristian Hartikainen +4
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…
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
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…
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran +2
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators. However, such h…