Atlanta, US
20th Sept, 2023
AI-based style transfer

Haotian Xue 薛昊天

I am a 2nd-year Ph.D. student working on Machine Learning at ML@GaTech, advised by Prof. Yongxin Chen. Previously, I obtained my B.E. of Computer Science from Shanghai Jiao Tong University with honor in 2022. I worked as research intern at MIT CSAIL supervised by Josh Tenenbaum, working closely with Yunzhu Li and Fish Tung.

  • [2023.09] Diff-PGD and 3D-IntPhys are accepted by NeurIPS'2023!
  • [2023.08] I was invited as a reviewer for ICLR'2024.
  • [2023.05] We propose Diff-PGD, a diffusion-based adv-sample generation framework.
  • [2022.12] I am selected as the Top Reviewer of NeurIPS 2022.
  • [2022.10] Our Distance-Transformer is accepted to EMNLP2022 Findings.
  • [2022.08] I will start as a PhD student at (ML@GT) starting from 2022Fall.
Research Interest

My research interest lies in broad aspects of Machine Learning, Computer Vision and Natural Language Processing. Currenty, I target myself to the following directions:

  • Generative models + X: utilize/learn strong prior knowledge using Generative Models (e.g. GAN, Diffusion Models), to empower AI problem, including robust AI, robot learning and inverse problem
  • Compositional and Explainable AI: learning Compositional and Explainable representation or network stuctures for deep learning models in e.g. Computer Vision and Natural Language Processing
Research Experience
  • [2023-current]: Start collaborating with Prof. Animesh Garg, GaTech, on DM+RL
  • [2023-current]: Start collaborating with Prof. Bin Hu, UIUC, on DM+Robustness
  • [2022-current]: Work as a GRA Ph.D. student at FLAIR lab with Prof. Yongxin Chen
  • [2022-2023]: Work as a remote intern at MIT CSAIL, advised by Josh Tenenbaum, Yunzhu Li and Fish Tung
  • [2021-2021]: Work as a research intern at NLC group, Microsoft Research
  • [2021-2022]: Work as a research intern at John Hopcroft Center, advised by Prof. Zhouhan Lin on NLP
  • [2020-2021]: Work as a research intern at John Hopcroft Center, advised by Prof. Quanshi Zhang on XAI
Reviewer Experience

#Conference(#Paper Number)

ICML'22(2), NeurIPS'22(4), ICML'23(2), NeurIPS'23(6), ICLR'24(?)

Publications ( show selected / show all by date / show all by topic )

Topics: Explainable AI / Vision / NLP/ Generative Model (*/†: indicates equal contribution.)

Towards More Effective Protection Against Diffusion-Based Mimicry with Score Distilation
Haotian Xue, Chumeng Liang*, Xiaoyu Wu*, Yongxin Chen

[In Preparation] [GitHub]

Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Haotian Xue, Alexandre Araujo, Bin Hu, Yongxin Chen

[NeurIPS 2023] [GitHub]

3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes
Haotian Xue, Antonio Torralba, Joshua B. Tenenbaum, Daniel LK Yamins, Yunzhu Li, Hsiao-Yu Tung

[NeurIPS 2023] [AIhub] [Poster]

Syntax-guided Localized Self-attention by Constituency Syntactic Distance
Shengyuan Hou*, Haotian Xue*, Jushi Kai*, Bingyu Zhu, Bo Yuan, Longtao Huang, Xinbin Wang, Zhouhan Lin

[EMNLP2022, Findings]

Learning to Adaptively Incorporate External Syntax through Gated Self-Attention

[In Submission to ACL2023]

A Hypothesis For The Cognitive Difficulty of Images
Xu Chen*, Xin Wang*, Haotian Xue, Zhengyang Liang, Xin Jin, Quanshi Zhang,


Evaluation of Attribution Explanations without Ground Truth
Hao Zhang, Haotian Xue, Jiayi Chen, Yiting Chen, Wen Shen, Quanshi Zhang,


Active Adversarial Learning
Haotian Xue, Nanyang Ye

[Bachelor Thesis]