Publications (11)
Scalable Bilinear Learning Using State and Action Features
Yichen Chen, Lihong Li, Mengdi Wang
Approximate linear programming (ALP) represents one of the major algorithmic families to solve large-scale Markov decision processes (MDP). In this work, we study a primal-dual for…
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning
Yichen Chen, Mengdi Wang
We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minim…
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
Weiyu Feng, Seth Z. Zhao, Chuanyu Pan +4
Digital twin is a problem of augmenting real objects with their digital counterparts. It can underpin a wide range of applications in augmented reality (AR), autonomy, and UI/UX. A…
Lower Bound On the Computational Complexity of Discounted Markov Decision Problems
Yichen Chen, Mengdi Wang
We study the computational complexity of the infinite-horizon discounted-reward Markov Decision Problem (MDP) with a finite state space and a finite action space $|…
Recent Methodological Advances in Federated Learning for Healthcare
Fan Zhang, Daniel Kreuter, Yichen Chen +10
For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the util…
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
Yichen Chen, Siying Li, Yuhang Liang +2
Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding…