3 citations · 3 across the 9 of their papers we have counts for
9 papers
Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery
Zhe Zhao, Haibin Wen, Yingcheng Wu +10
Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces. Today, top scientific capabilities remain siloed--one AI system for biological a…
Qumus: Realization of An Embodied AI Quantum Material Experimentalist
Lihan Shi, Zhaoyi Joy Zheng, Xinzhe Juan +14
While modern Large Language Models (LLMs) and agentic artificial intelligence (AI) have demonstrated transformative capabilities in digital domains, the realization of embodied AI…
Autogenesis: A Self-Evolving Agent Protocol
Wentao Zhang, Zhe Zhao, Haibin Wen +4
LLM-based agent systems can adapt by revising prompts, constructing tools, and changing other runtime resources. Managing these heterogeneous updates requires a shared lifecycle fo…
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures
Ming Yin, Mengdi Wang, Yu-Xiang Wang
This article reviews the recent advances on the statistical foundation of reinforcement learning (RL) in the offline and low-adaptive settings. We will start by arguing why offline…
Fast Best-of-N Decoding via Speculative Rejection
Hanshi Sun, Momin Haider, Ruiqi Zhang +6
The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human p…
Relative Translation Invariant Wasserstein Distance
Binshuai Wang, Qiwei Di, Ming Yin +3
Motivated by the Bures distance, we introduce a new family of distances, \emph{relative translation invariant Wasserstein distances}, denoted by , as an extension of the clas…
Offline Multitask Representation Learning for Reinforcement Learning
Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng +4
We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from different tasks that share a common repr…
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
Nikki Lijing Kuang, Ming Yin, Mengdi Wang +2
Recent studies in reinforcement learning (RL) have made significant progress by leveraging function approximation to alleviate the sample complexity hurdle for better performance.…
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Ming Yin, Yaqi Duan, Mengdi Wang +1
Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. D…