26 citations · 39 across the 21 of their papers we have counts for
21 papers
(Mis)Understanding Benign Overfitting in Equity Return Prediction
Hui Guo, Jiawei Huang, Runze Li +1
Highly overparameterized models often predict well despite interpolating training data in complex domains, challenging the classical bias--variance tradeoff. We investigate whether…
Secure Low-altitude Maritime Communications via Intelligent Jamming
Jiawei Huang, Aimin Wang, Geng Sun +5
Low-altitude wireless networks (LAWNs) have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles (UAVs) serve as practical lo…
Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models
Yuhua Jiang, Jiawei Huang, Yufeng Yuan +4
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing Large Language Models (LLMs) on complex reasoning tasks. However, existing methods suffer f…
Steering No-Regret Agents in MFGs under Model Uncertainty
Leo Widmer, Jiawei Huang, Niao He
Incentive design is a popular framework for guiding agents' learning dynamics towards desired outcomes by providing additional payments beyond intrinsic rewards. However, most exis…
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Jiawei Huang, Bingcong Li, Christoph Dann +1
Sample efficiency is critical for online Reinforcement Learning from Human Feedback (RLHF). While existing works investigate sample-efficient online exploration strategies, the pot…
Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning
Jiawei Huang, Aimin Wang, Geng Sun +4
Low Earth orbit (LEO) satellites can be used to assist maritime wireless communications for wide-area data transmission. However, the extensive coverage of LEO satellites, combined…
Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming
Jiawei Huang, Aimin Wang, Geng Sun +4
Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely…
Learning to Steer Markovian Agents under Model Uncertainty
Jiawei Huang, Vinzenz Thoma, Zebang Shen +2
Designing incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards…
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash…
On Robust Wasserstein Barycenter: The Model and Algorithm
Xu Wang, Jiawei Huang, Qingyuan Yang +1
The Wasserstein barycenter problem is to compute the average of given probability measures, which has been widely studied in many different areas; however, real-world data sets…
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
Jiawei Huang, Batuhan Yardim, Niao He
In this paper, we study the fundamental statistical efficiency of Reinforcement Learning in Mean-Field Control (MFC) and Mean-Field Game (MFG) with general model-based function app…
Company Competition Graph
Yanci Zhang, Yutong Lu, Haitao Mao +4
Financial market participants frequently rely on numerous business relationships to make investment decisions. Investors can learn about potential risks and opportunities associate…
Coresets for Wasserstein Distributionally Robust Optimization Problems
Ruomin Huang, Jiawei Huang, Wenjie Liu +1
Wasserstein distributionally robust optimization (\textsf{WDRO}) is a popular model to enhance the robustness of machine learning with ambiguous data. However, the complexity of \t…
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification
Zhixin Yan, Jiawei Huang, Kehua Xiang
To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extr…
A Novel Sequential Coreset Method for Gradient Descent Algorithms
Jiawei Huang, Ruomin Huang, Wenjie Liu +2
A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a…
Is Simple Uniform Sampling Effective for Center-Based Clustering with Outliers: When and Why?
Jiawei Huang, Wenjie Liu, Hu Ding
Real-world datasets often contain outliers, and the presence of outliers can make the clustering problems to be much more challenging. In this paper, we propose a simple uniform sa…
Defending SVMs against Poisoning Attacks: the Hardness and DBSCAN Approach
Hu Ding, Fan Yang, Jiawei Huang
Adversarial machine learning has attracted a great amount of attention in recent years. In a poisoning attack, the adversary can inject a small number of specially crafted samples…
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery
Hu Ding, Ruizhe Qin, Jiawei Huang
In this paper, we consider robust optimization problems in high dimensions. Because a real-world dataset may contain significant noise or even specially crafted samples from some a…
The Effectiveness of Uniform Sampling for Center-Based Clustering with Outliers
Hu Ding, Jiawei Huang, Haikuo Yu
Clustering has many important applications in computer science, but real-world datasets often contain outliers. Moreover, the presence of outliers can make the clustering problems…
Learning to Cluster for Proposal-Free Instance Segmentation
Yen-Chang Hsu, Zheng Xu, Zsolt Kira +1
This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering. We applied the approach to instance segmentation, which i…
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
There is an increasing interest on accelerating neural networks for real-time applications. We study the student-teacher strategy, in which a small and fast student network is trai…