4 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.LG2023★ 4 cited
Learning to Generate Better Than Your LLM
Jonathan D. Chang, Kiante Brantley, Rajkumar Ramamurthy +2
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT and GPT-…
cs.LG2022★ 1 cited
Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan D. Chang, Kaiwen Wang, Nathan Kallus +1
We study representation learning for Offline Reinforcement Learning (RL), focusing on the important task of Offline Policy Evaluation (OPE). Recent work shows that, in contrast to…
cs.CL2017
Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks
Jonathan Chang, Stefan Scherer
Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker tha…