23 citations · 37 across the 23 of their papers we have counts for
12 papers · 1 filter
Topological Federated Clustering via Gravitational Potential Fields under Local Differential Privacy
Yunbo Long, Jiaquan Zhang, Xi Chen +1
Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy…
EQ-Negotiator: Dynamic Emotional Personas Empower Small Language Models for Edge-Deployable Credit Negotiation
Yunbo Long, Yuhan Liu, Alexandra Brintrup
The deployment of large language models (LLMs) in automated negotiation has set a high performance benchmark, but their computational cost and data privacy requirements render them…
Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks
Lukas Beckenbauer, Johannes-Lucas Loewe, Ge Zheng +1
Complex, non-linear tasks challenge LLM-enhanced multi-agent systems (MAS) due to partial observability and suboptimal coordination. We propose Orchestrator, a novel MAS framework…
EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation
Yunbo Long, Liming Xu, Lukas Beckenbauer +2
Recent research on Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) has demonstrated that agents can engage in \textit{complex}, \textit{multi-turn} negotiations, o…
SynDelay: A Synthetic Dataset for Delivery Delay Prediction
Liming Xu, Yunbo Long, Alexandra Brintrup
Artificial intelligence (AI) is transforming supply chain management, yet progress in predictive tasks -- such as delivery delay prediction -- remains constrained by the scarcity o…
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data
Yunbo Long, Liming Xu, Ge Zheng +1
Federated learning (FL) enables retailers to share model parameters for demand forecasting while maintaining privacy. However, heterogeneous data across diverse regions, driven by…
Efficient and Privacy-Preserved Link Prediction via Condensed Graphs
Yunbo Long, Liming Xu, Alexandra Brintrup
Link prediction is crucial for uncovering hidden connections within complex networks, enabling applications such as identifying potential customers and products. However, this rese…
An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning
Ge Zheng, Alexandra Brintrup
In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabili…
EmoDebt: Bayesian-Optimized Emotional Intelligence for Strategic Agent-to-Agent Debt Recovery
Yunbo Long, Yuhan Liu, Liming Xu +1
The emergence of autonomous Large Language Model (LLM) agents has created a new ecosystem of strategic, agent-to-agent interactions. However, a critical challenge remains unaddress…
TuneNSearch: a hybrid transfer learning and local search approach for solving vehicle routing problems
Arthur Corrêa, Cristóvão Silva, Liming Xu +2
This paper introduces TuneNSearch, a hybrid transfer learning and local search approach for addressing diverse variants of the vehicle routing problem (VRP). Our method uses reinfo…
LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion
Yunbo Long, Liming Xu, Alexandra Brintrup
Synthetic tabular data are increasingly being used to replace real data, serving as an effective solution that simultaneously protects privacy and addresses data scarcity. However,…
Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
Yunbo Long, Liming Xu, Alexandra Brintrup
Current evaluations of synthetic tabular data mainly focus on how well joint distributions are modeled, often overlooking the assessment of their effectiveness in preserving realis…