Haokun Chen
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View article: SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence Open
The rapid progress of Large Language Models has advanced agentic systems in decision-making, coordination, and task execution. Yet, existing agentic system generation frameworks lack full autonomy, missing from-scratch agent generation, se…
View article: FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models
FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models Open
Multimodal Large Language Models (MLLMs) excel in tasks like multimodal reasoning and cross-modal retrieval but face deployment challenges in real-world scenarios due to distributed multimodal data and strict privacy requirements. Federate…
View article: FedPop: Federated Population-based Hyperparameter Tuning
FedPop: Federated Population-based Hyperparameter Tuning Open
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to conventional ML pipelines, the client local optimization a…
View article: Building Variable-Sized Models via Learngene Pool
Building Variable-Sized Models via Learngene Pool Open
Recently, Stitchable Neural Networks (SN-Net) is proposed to stitch some pre-trained networks for quickly building numerous networks with different complexity and performance trade-offs. In this way, the burdens of designing or training th…
View article: Building Variable-sized Models via Learngene Pool
Building Variable-sized Models via Learngene Pool Open
Recently, Stitchable Neural Networks (SN-Net) is proposed to stitch some pre-trained networks for quickly building numerous networks with different complexity and performance trade-offs. In this way, the burdens of designing or training th…
View article: FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning Open
Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a substantial amount of data for finetuning. However, collectin…
View article: FedPop: Federated Population-based Hyperparameter Tuning
FedPop: Federated Population-based Hyperparameter Tuning Open
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to conventional ML pipelines, the client local optimization a…
View article: Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation Open
Recently, privacy issues in web services that rely on users' personal data have raised great attention. Unlike existing privacy-preserving technologies such as federated learning and differential privacy, we explore another way to mitigate…
View article: Towards Data-Free Domain Generalization
Towards Data-Free Domain Generalization Open
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning. In particular, we address the question: How can knowledge contained in models trained on different source domains be merged into…
View article: Intent Preference Decoupling for User Representation on Online Recommender System
Intent Preference Decoupling for User Representation on Online Recommender System Open
Accurately characterizing the user's current interest is the core of recommender systems. However, users' interests are dynamic and affected by intent factors and preference factors. The intent factors imply users' current needs and change…
View article: Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning Open
Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal with the dynamic user preference and optimize accumulative u…
View article: Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient
Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient Open
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items t…
View article: Large-scale Interactive Recommendation with Tree-structured Policy Gradient
Large-scale Interactive Recommendation with Tree-structured Policy Gradient Open
Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items t…
View article: Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling Open
Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networks and multi-armed …
View article: Neural Link Prediction over Aligned Networks
Neural Link Prediction over Aligned Networks Open
Link prediction is a fundamental problem with a wide range of applications in various domains, which predicts the links that are not yet observed or the links that may appear in the future. Most existing works in this field only focus on m…
View article: A Quantification Model for the Structure of Clay Materials
A Quantification Model for the Structure of Clay Materials Open
Background In this paper, the quantification for clay structure is explicitly explained, and the approach and goals of quantification are also discussed. The authors consider that the purpose of the quantification for clay structure is to …