no code implementations • 6 Nov 2023 • Haoyang Cao, Haotian Gu, Xin Guo, Mathieu Rosenbaum
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
no code implementations • 25 Jul 2023 • Haoyang Cao, Haotian Gu, Xin Guo, Mathieu Rosenbaum
In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of "transferability"; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings.
no code implementations • 22 May 2023 • 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.
no code implementations • 27 Jan 2023 • Haoyang Cao, Haotian Gu, Xin Guo, Mathieu Rosenbaum
In this paper we build for the first time, to the best of our knowledge, a mathematical framework for the general procedure of transfer learning.
no code implementations • 14 Dec 2022 • Qinkai Chen, Mohamed El-Mennaoui, Antoine Fosset, Amine Rebei, Haoyang Cao, Philine Bouscasse, Christy Eóin O'Beirne, Sasha Shevchenko, Mathieu Rosenbaum
With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge.
no code implementations • 11 Feb 2022 • Haoyang Cao, Xin Guo, Guan Wang
Anomaly detection has been an active research area with a wide range of potential applications.
no code implementations • NeurIPS 2021 • Haoyang Cao, Samuel N. Cohen, Lukasz Szpruch
Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions.
no code implementations • 25 Apr 2021 • Haoyang Cao, Xin Guo
Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention.
no code implementations • 3 Jun 2020 • Haoyang Cao, Xin Guo
This paper analyzes the training process of GANs via stochastic differential equations (SDEs).
no code implementations • 10 Feb 2020 • 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.