3 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
Provable Test-Time Scaling for Beam Search in LLM Reasoning
Qijia He, Yu Huang, Yuan Cheng +2
Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning paths early,…
Transformers Provably Learn Directed Acyclic Graphs via Kernel-Guided Mutual Information
Yuan Cheng, Yu Huang, Zhe Xiong +2
Uncovering hidden graph structures underlying real-world data is a critical challenge with broad applications across scientific domains. Recently, transformer-based models leveragi…
Sample Complexity Characterization for Linear Contextual MDPs
Junze Deng, Yuan Cheng, Shaofeng Zou +1
Contextual Markov decision processes (CMDPs) describe a class of reinforcement learning problems in which the transition kernels and reward functions can change over time with diff…
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes
Ruiquan Huang, Yuan Cheng, Jing Yang +2
In multi-task reinforcement learning (RL) under Markov decision processes (MDPs), the presence of shared latent structures among multiple MDPs has been shown to yield significant b…
In-Context Convergence of Transformers
Yu Huang, Yuan Cheng, Yingbin Liang
Transformers have recently revolutionized many domains in modern machine learning and one salient discovery is their remarkable in-context learning capability, where models can sol…