11 citations · 25 across the 15 of their papers we have counts for
15 papers
Stability of Differential Stochastic Variational Inequalities with History-Dependent Responses and Transfer Learning
Xiaojun Chen, Jian Guo, Xin Guo +1
In this paper, we propose and study a class of differential stochastic variational inequalities (DSVIs), in which an ordinary differential equation (ODE) is coupled with history-de…
Sample Complexity of Transfer Learning: An Optimal Transport Approach
Haoyang Cao, Xin Guo, Wenpin Tang +1
Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI. The essence of transfer learn…
Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR
Utkarsh Tyagi, Xingang Guo, MohammadHossein Rezaei +5
Reinforcement learning with verifiable rewards has made post-training highly effective when correctness can be checked automatically. However, many important model behaviors requir…
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…
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Haoyang Cao, Xin Guo, Guan Wang
Anomaly detection has been an active research area with a wide range of potential applications. Key challenges for anomaly detection in the AI era with big data include lack of pri…
Approximation of N-player stochastic games with singular controls by mean field games
Haoyang Cao, Xin Guo, Joon Seok Lee
This paper establishes that a class of -player stochastic games with singular controls, either of bounded velocity or of finite variation, can both be approximated by mean field…
Convergence of GANs Training: A Game and Stochastic Control Methodology
Othmane Mounjid, Xin Guo
Training generative adversarial networks (GANs) is known to be difficult, especially for financial time series. This paper first analyzes the well-posedness problem in GANs minimax…
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…
Generative Adversarial Network: Some Analytical Perspectives
Haoyang Cao, Xin Guo
Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention. Over the past years, different variations of GANs models have been devel…
SDE approximations of GANs training and its long-run behavior
Haoyang Cao, Xin Guo
This paper analyzes the training process of GANs via stochastic differential equations (SDEs). It first establishes SDE approximations for the training of GANs under stochastic gra…
Connecting GANs, MFGs, and OT
Haoyang Cao, Xin Guo, Mathieu Laurière
Generative adversarial networks (GANs) have enjoyed tremendous success in image generation and processing, and have recently attracted growing interests in financial modelings. Thi…
Nonzero-sum stochastic games and mean-field games with impulse controls
Matteo Basei, Haoyang Cao, Xin Guo
We consider a general class of nonzero-sum -player stochastic games with impulse controls, where players control the underlying dynamics with discrete interventions. We adopt a…