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.

News
  • [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
[TBA]

[In Submission to ACL2023]

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

[Arxiv]

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

[OpenReview]

Active Adversarial Learning
Haotian Xue, Nanyang Ye

[Bachelor Thesis]