16 citations · 35 across the 12 of their papers we have counts for
12 papers
Transportation Marketplace Rate Forecast Using Signature Transform
Haotian Gu, Xin Guo, Timothy L. Jacobs +2
Freight transportation marketplace rates are typically challenging to forecast accurately. In this work, we have developed a novel statistical technique based on signature transfor…
Risk of Transfer Learning and its Applications in Finance
Haoyang Cao, Haotian Gu, Xin Guo +1
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. In this paper, we propos…
Transfer Learning for Portfolio Optimization
Haoyang Cao, Haotian Gu, Xin Guo +1
In this work, we explore the possibility of utilizing transfer learning techniques to address the financial portfolio optimization problem. We introduce a novel concept called "tra…
Feasibility of Transfer Learning: A Mathematical Framework
Haoyang Cao, Haotian Gu, Xin Guo
Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. It has enjoyed numerous empirical succ…
Feasibility and Transferability of Transfer Learning: A Mathematical Framework
Haoyang Cao, Haotian Gu, Xin Guo +1
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. Despite its numerous emp…
Multi-level Adaptation for Automatic Landing with Engine Failure under Turbulent Weather
Haotian Gu, Hamidreza Jafarnejadsani
This paper addresses efficient feasibility evaluation of possible emergency landing sites, online navigation, and path following for automatic landing under engine-out failure subj…
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
Haotian Gu, Xin Guo, Xiaoli Wei +1
One of the challenges for multi-agent reinforcement learning (MARL) is designing efficient learning algorithms for a large system in which each agent has only limited or partial in…
Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation
Haotian Gu, Xin Guo, Xinyu Li
Adversarial training has gained great popularity as one of the most effective defenses for deep neural network and more generally for gradient-based machine learning models against…
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Haotian Gu, Xin Guo, Xiaoli Wei +1
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality. This paper builds the mathematical framework to a…
Dynamic Programming Principles for Mean-Field Controls with Learning
Haotian Gu, Xin Guo, Xiaoli Wei +1
Dynamic programming principle (DPP) is fundamental for control and optimization, including Markov decision problems (MDPs), reinforcement learning (RL), and more recently mean-fiel…
Error estimates for a POD method for solving viscous G-equations in incompressible cellular flows
Haotian Gu, Jack Xin, Zhiwen Zhang
The G-equation is a well-known model for studying front propagation in turbulent combustion. In this paper, we develop an efficient model reduction method for computing \textcolor{…
Analysis on hybrid fractals
Patricia Alonso Ruiz, Yuming Chen, Haotian Gu +2
We introduce hybrid fractals as a class of fractals constructed by gluing several fractal pieces in a specific manner and study energy forms and Laplacians on them. We consider in…