Sample Complexity of Kernel-Based Q-Learning

no code implementations • 1 Feb 2023 • Sing-Yuan Yeh, Fu-Chieh Chang, Chang-Wei Yueh, Pei-Yuan Wu, Alberto Bernacchia, Sattar Vakili

To the best of our knowledge, this is the first result showing a finite sample complexity under such a general model.

Q-Learning Reinforcement Learning (RL)

Flexible Multiple-Objective Reinforcement Learning for Chip Placement

no code implementations • 13 Apr 2022 • Fu-Chieh Chang, Yu-Wei Tseng, Ya-Wen Yu, Ssu-Rui Lee, Alexandru Cioba, I-Lun Tseng, Da-Shan Shiu, Jhih-Wei Hsu, Cheng-Yuan Wang, Chien-Yi Yang, Ren-Chu Wang, Yao-Wen Chang, Tai-Chen Chen, Tung-Chieh Chen

Recently, successful applications of reinforcement learning to chip placement have emerged.

reinforcement-learning Reinforcement Learning (RL)

G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data

no code implementations • 29 May 2019 • Fu-Chieh Chang, Hao-Jen Wang, Chun-Nan Chou, Edward Y. Chang

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications.

General Classification

Representation Learning on Large and Small Data

no code implementations • 25 Jul 2017 • Chun-Nan Chou, Chuen-Kai Shie, Fu-Chieh Chang, Jocelyn Chang, Edward Y. Chang

Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation.

Melanoma Diagnosis Representation Learning +1