26 citations · 39 across the 8 of their papers we have counts for
8 papers
DLM-One: Diffusion Language Models for One-Step Sequence Generation
Tianqi Chen, Shujian Zhang, Mingyuan Zhou
This paper introduces DLM-One, a score-distillation-based framework for one-step sequence generation with continuous diffusion language models (DLMs). DLM-One eliminates iterative…
Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models
Tianqi Chen, Shujian Zhang, Mingyuan Zhou
The machine learning community is increasingly recognizing the importance of fostering trust and safety in modern generative AI (GenAI) models. We posit machine unlearning (MU) as…
Switchable Decision: Dynamic Neural Generation Networks
Shujian Zhang, Korawat Tanwisuth, Chengyue Gong +2
Auto-regressive generation models achieve competitive performance across many different NLP tasks such as summarization, question answering, and classifications. However, they are…
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Shentao Yang, Shujian Zhang, Congying Xia +3
Aligning language models (LMs) with preferences is an important problem in natural language generation. A key challenge is that preferences are typically provided at the sequence l…
AutoML-GPT: Automatic Machine Learning with GPT
Shujian Zhang, Chengyue Gong, Lemeng Wu +2
AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human effort…
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Yihao Feng, Shentao Yang, Shujian Zhang +4
When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve user-specific goals. Pri…
A Unified Framework for Alternating Offline Model Training and Policy Learning
Shentao Yang, Shujian Zhang, Yihao Feng +1
In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and subsequently utilize the learned model and fixed datase…
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning
Shentao Yang, Yihao Feng, Shujian Zhang +1
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learning from static datasets, without interacting with the underlying environment dur…