10 citations · 19 across the 7 of their papers we have counts for
9 papers
Hybrid Reasoning Agents
Jiaru Zou, Yunzhe Qi, Sirui Chen +5
How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while…
AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning
Jiaru Zou, Ling Yang, Yunzhe Qi +5
Agentic reinforcement learning has advanced large language models (LLMs) to reason through long chain-of-thought trajectories while interleaving external tool use. Existing approac…
Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
Jiaru Zou, Yikun Ban, Zihao Li +4
Large language models are typically adapted to downstream tasks through supervised fine-tuning on domain-specific data. While standard fine-tuning focuses on minimizing generation…
PageRank Bandits for Link Prediction
Yikun Ban, Jiaru Zou, Zihao Li +5
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been dire…
Meta Clustering of Neural Bandits
Yikun Ban, Yunzhe Qi, Tianxin Wei +2
The contextual bandit has been identified as a powerful framework to formulate the recommendation process as a sequential decision-making process, where each item is regarded as an…
Neural Contextual Bandits for Personalized Recommendation
Yikun Ban, Yunzhe Qi, Jingrui He
In the dynamic landscape of online businesses, recommender systems are pivotal in enhancing user experiences. While traditional approaches have relied on static supervised learning…
Graph Neural Bandits
Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information. Various bandit algorithms have b…
Neural Bandit with Arm Group Graph
Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information. Motivated by the fact that the arms usually ex…
Neural Collaborative Filtering Bandits via Meta Learning
Yikun Ban, Yunzhe Qi, Tianxin Wei +1
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.…