8 citations · 18 across the 15 of their papers we have counts for
15 papers
Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation
Hang Ren, Yulin Wu, Shuhan Qi +4
Regret minimization is a powerful method for finding Nash equilibria in Normal-Form Games (NFGs) and Extensive-Form Games (EFGs), but it typically guarantees convergence only for t…
MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models
Bohan Jin, Shuhan Qi, Kehai Chen +2
The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to…
Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
Dingkun Zhang, Shuhan Qi, Xinyu Xiao +2
Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities. Considering the heavy cost of training…
Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
Yizhou Luo, Qiang Wang, Shaohuai Shi +4
Deep learning (DL) has demonstrated significant success across diverse fields, leading to the construction of dedicated GPU accelerators within GPU clusters for high-quality traini…
KnobTree: Intelligent Database Parameter Configuration via Explainable Reinforcement Learning
Jiahan Chen, Shuhan Qi, Yifan Li +4
Databases are fundamental to contemporary information systems, yet traditional rule-based configuration methods struggle to manage the complexity of real-world applications with hu…
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning
Shuhan Qi, Shuhao Zhang, Qiang Wang +3
Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popul…
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
Shaohuai Shi, Qing Yang, Yang Xiang +2
To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for…
Listening to Users' Voice: Automatic Summarization of Helpful App Reviews
Cuiyun Gao, Yaoxian Li, Shuhan Qi +4
App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features…
Dynamic Contrastive Distillation for Image-Text Retrieval
Jun Rao, Liang Ding, Shuhan Qi +4
Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major d…
Parameter-Efficient and Student-Friendly Knowledge Distillation
Jun Rao, Xv Meng, Liang Ding +2
Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixe…
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning
Shuhan Qi, Shuhao Zhang, Xiaohan Hou +3
Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor s…
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
Jun Rao, Fei Wang, Liang Ding +4
This article aims to provide the information retrieval community with some reflections on recent advances in retrieval learning by analyzing the reproducibility of image-text retri…
D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network
Huale Li, Xuan Wang, Zengyue Guo +2
Counterfactual Regret Minimization (CFR)} is the popular method for finding approximate Nash equilibrium in two-player zero-sum games with imperfect information. CFR solves games b…
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Huale Li, Xuan Wang, Fengwei Jia +4
Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike existing studies…
Solving imperfect-information games via exponential counterfactual regret minimization
Huale Li, Xuan Wang, Shuhan Qi +4
In general, two-agent decision-making problems can be modeled as a two-player game, and a typical solution is to find a Nash equilibrium in such game. Counterfactual regret minimiz…