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Risk of Transfer Learning and its Applications in Finance
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
Transfer Learning for Portfolio Optimization
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.
Feasibility of Transfer Learning: A Mathematical Framework
Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
Feasibility and Transferability of Transfer Learning: A Mathematical Framework
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.
Towards mapping the contemporary art world with ArtLM: an art-specific NLP model
With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge.
Meta-learning with GANs for anomaly detection, with deployment in high-speed rail inspection system
Anomaly detection has been an active research area with a wide range of potential applications.
Identifiability in inverse reinforcement learning
Inverse reinforcement learning attempts to reconstruct the reward function in a Markov decision problem, using observations of agent actions.
Generative Adversarial Network: Some Analytical Perspectives
Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention.
SDE approximations of GANs training and its long-run behavior
This paper analyzes the training process of GANs via stochastic differential equations (SDEs).
Connecting GANs, MFGs, and OT
Generative adversarial networks (GANs) have enjoyed tremendous success in image generation and processing, and have recently attracted growing interests in financial modelings.
