add: 1 neurips paper

_bibliography/pubs.bib

@@ -1,6 +1,13 @@
 ---
 ---
 
+@inproceedings{zhou2023generating,
+  title={Generating and Distilling Discrete Adversarial Examples from Large-Scale Models},
+  author={Zhou, Andy and Wang, Jindong and Wang, Yu-Xiong and Wang, Haohan},
+  journal={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2023},
+}
+
 @article{wang2023exploring,
   title={Exploring Vision-Language Models for Imbalanced Learning},
   author={Wang, Yidong and Yu, Zhuohao and Wang, Jindong and Heng, Qiang and Chen, Hao and Ye, Wei and Xie, Rui and Xie, Xing and Zhang, Shikun},
@@ -134,6 +141,7 @@ @inproceedings{wang2023robustness
   corr={true},
   code={https://github.com/microsoft/robustlearn},
   zhihu={https://zhuanlan.zhihu.com/p/612391048},
+  special={Highlighted paper}
 }
 
 @inproceedings{lu2023towards,

_news/neurips23.md

@@ -0,0 +1,7 @@
+---
+layout: post
+date: 2023-09-23
+inline: true
+---
+
+Paper "Generating and Distilling Discrete Adversarial Examples from Large-Scale Models" is accepted by NeurIPS 2023. 

_pages/research.md

@@ -35,6 +35,7 @@ Open source:
 
 ##### Out-of-distribution (Domain) generalization and adaptation for distribution shift
 
+- **[NeurIPS'23]** Generating and Distilling Discrete Adversarial Examples from Large-Scale Models. Andy Zhou, Jindong Wang, Yu-Xiong Wang, Haohan Wang.
 - **[ICCV'23]** Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning. Kaijie Zhu, Xixu Hu, Jindong Wang, Xing Xie, Ge Yang.
 - **[ICLR'23]** [Out-of-distribution Representation Learning for Time Series Classification](https://arxiv.org/abs/2209.07027). Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, and Xing Xie.
 - **[KDD'23]** [Domain-Specific Risk Minimization for Out-of-Distribution Generalization](https://arxiv.org/pdf/2208.08661.pdf). YiFan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, Xing Xie, and Dacheng Tao.