add: links

_pages/about.md

@@ -21,7 +21,7 @@ jindongwang [at] outlook.com, jindong.wang [at] microsoft.com<br>
 
 I'm currently a Senior Researcher at [Microsoft Research Asia (MSRA)](http://www.msra.cn/), in a group managed by [Xing Xie](https://www.microsoft.com/en-us/research/people/xingx/). Before joining MSRA, I obtained my Ph.D. from Institute of Computing Technology, Chinese Academy of Sciences in June, 2019. My doctoral thesis was awarded the excellent Ph.D. thesis of Chinese Academy of Sciences. In 2018/04--2018/08, I was a visitor of Prof. [Qiang Yang](https://cse.hkust.edu.hk/~qyang/)'s group at Hong Kong University of Science and Technology (HKUST). My work on transfer learning won the best paper awards in ICCSE 2018 and FTL-IJCAI 2019. In 2021, I published the textbook [Introduction to Transfer Learning](http://jd92.wang/tlbook), a hands-on introduction to transfer learning. In 2022, I was selected as one of the [2022 AI 2000 Most Influential Scholars](https://www.aminer.cn/ai2000?domain_ids=5dc122672ebaa6faa962c2a4) by AMiner between 2012-2021 (ranked 49/2000). Four of my first-author papers are ranked by Google Scholar as [highly-cited papers](https://zhuanlan.zhihu.com/p/421192644). I gave tutorials at [IJCAI'22](https://dgresearch.github.io/), WSDM 2023, and KDD 2023.
 
-**Research interest:** robust machine learning, out-of-distribution / domain generalization, transfer learning, semi-supervised learning, federated learning, and related applications such as activity recognition and computer vision. These days, I'm particularly interested in Large Language Models (LLMs) robustness. See this [page](https://jd92.wang/research/) for more details. *Interested in [internship](https://zhuanlan.zhihu.com/p/102558267) or collaboration? Contact me.*
+**Research interest:** robust machine learning, out-of-distribution / domain generalization, transfer learning, semi-supervised learning, federated learning, and related applications such as activity recognition and computer vision. These days, I'm particularly interested in Large Language Models (LLMs) [evaluation](https://llm-eval.github.io/) and [robustness enhancement](https://llm-enhance.github.io/). See this [page](https://jd92.wang/research/) for more details. *Interested in [internship](https://zhuanlan.zhihu.com/p/102558267) or collaboration? Contact me.*
 
 **Announcement:** I'm experimenting a new form of research collaboration. You can click [here](https://forms.office.com/r/32Fs6uAjT6) if you are interested!
 

_pages/publications.md

@@ -17,14 +17,14 @@ nav: true
 - PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization. Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang. [[arxiv](https://arxiv.org/abs/2306.05087)] [[code](https://github.com/WeOpenML/PandaLM)]
 - Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup. Damien Teney, Jindong Wang, Ehsan Abbasnejad. [[arxiv](https://arxiv.org/abs/2305.16817)]
 - Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations. Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj. [[arxiv](https://arxiv.org/abs/2305.12715)]
-- Exploring Vision-Language Models for Imbalanced Learning. Yidong Wang, Zhuohao Yu, **Jindong Wang**, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang. [[arxiv](https://arxiv.org/abs/2304.01457)] [[code](https://github.com/Imbalance-VLM/Imbalance-VLM)]
-- An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning. Hao Chen, Yue Fan, Yidong Wang, **Jindong Wang**, Bernt Schiele, Xing Xie, Marios Savvides, Bhiksha Raj. [[arxiv](https://arxiv.org/abs/2211.11086)] 
-- FIXED: Frustratingly Easy Domain Generalization with Mixup. Wang Lu, **Jindong Wang**, Han Yu, Lei Huang, Xiang Zhang, Yiqiang Chen, Xing Xie. [[arxiv](https://arxiv.org/abs/2211.05228)]
+- Exploring Vision-Language Models for Imbalanced Learning. Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang. [[arxiv](https://arxiv.org/abs/2304.01457)] [[code](https://github.com/Imbalance-VLM/Imbalance-VLM)]
+- An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning. Hao Chen, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Marios Savvides, Bhiksha Raj. [[arxiv](https://arxiv.org/abs/2211.11086)] 
+- FIXED: Frustratingly Easy Domain Generalization with Mixup. Wang Lu, Jindong Wang, Han Yu, Lei Huang, Xiang Zhang, Yiqiang Chen, Xing Xie. [[arxiv](https://arxiv.org/abs/2211.05228)]
 - Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets. Hao Chen, Ran Tao, Han Zhang, Yidong Wang, Wei Ye, Jindong Wang, Guosheng Hu, and Marios Savvides. [[arxiv](https://arxiv.org/abs/2208.07463)]
-- Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. Yivan Zhang, **Jindong Wang**, Xing Xie, and Masashi Sugiyama. [[arxiv](https://arxiv.org/abs/2208.02011)]
-- Boosting Cross-Domain Speech Recognition with Self-Supervision. Han Zhu, Gaofeng Cheng, **Jindong Wang**, Wenxin Hou, Pengyuan Zhang, and Yonghong Yan. [[arxiv](https://arxiv.org/abs/2206.09783)]
-- Learning Invariant Representations across Domains and Tasks. **Jindong Wang**, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu. [[arxiv](https://arxiv.org/abs/2103.05114)]
-- Learning to match distributions for domain adaptation. Chaohui Yu, **Jindong Wang**, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, and Tie-Yan Liu. [[arxiv](https://arxiv.org/abs/2007.10791)]
+- Equivariant Disentangled Transformation for Domain Generalization under Combination Shift. Yivan Zhang, Jindong Wang, Xing Xie, and Masashi Sugiyama. [[arxiv](https://arxiv.org/abs/2208.02011)]
+- Boosting Cross-Domain Speech Recognition with Self-Supervision. Han Zhu, Gaofeng Cheng, Jindong Wang, Wenxin Hou, Pengyuan Zhang, and Yonghong Yan. [[arxiv](https://arxiv.org/abs/2206.09783)]
+- Learning Invariant Representations across Domains and Tasks. Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu. [[arxiv](https://arxiv.org/abs/2103.05114)]
+- Learning to match distributions for domain adaptation. Chaohui Yu, Jindong Wang, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, and Tie-Yan Liu. [[arxiv](https://arxiv.org/abs/2007.10791)]
 
 #### Books
 

_pages/research.md

@@ -10,20 +10,26 @@ nav: true
 </center>
 
 The long-term research goal is to build robust models for modern AI, such as pre-trained models and large models. We create new theory, algorithms, applications, and open-sourced library to achieve our goal.
-These days, we are specifically interested in **robustness in large language models (LLMs)**.
+These days, we are specifically interested in **large language models (LLMs)** [evaluation](https://llm-eval.github.io/) and [robustness enhancement](https://llm-enhance.github.io/).
 
-Our research consists of the following topics with selected publications: [[View by year](https://jd92.wang/publications/)]
+Our research consists of the following topics with selected publications: [[View by year](https://jd92.wang/publications/)] [[Google scholar](https://scholar.google.com/citations?user=hBZ_tKsAAAAJ)]
 
 ##### New: large models
 
-- [arXiv'23] [EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus](https://arxiv.org/abs/2307.11760). Cheng Li, Jindong Wang, Kaijie Zhu, Yixuan Zhang, Wenxin Hou, Jianxun Lian, Xing Xie.
+Evaluation: (website: https://llm-eval.github.io/)
 - [arXiv'23] [A Survey on Evaluation of Large Language Models](https://arxiv.org/abs/2307.03109). Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Kaijie Zhu, Hao Chen, Linyi Yang, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie. [[code](https://github.com/MLGroupJLU/LLM-eval-survey)]
 - [arXiv'23] [PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts](https://arxiv.org/abs/2306.04528). Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil Zhenqiang Gong, Yue Zhang, Xing Xie. [[code](https://github.com/microsoft/promptbench)]
 - [arXiv'23] [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://arxiv.org/abs/2306.05087). Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang. [[code](https://github.com/WeOpenML/PandaLM)]
 - **[ACL'23 findings]** [GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective](https://arxiv.org/abs/2211.08073). Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang.
 - **[ICLR'23 large model workshop]** [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://arxiv.org/abs/2302.12095). Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, and Xing Xie.
-- [arXiv'23] [Exploring Vision-Language Models for Imbalanced Learning](https://arxiv.org/abs/2304.01457). Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang. [[code](https://github.com/Imbalance-VLM/Imbalance-VLM)]
-- Open source:
+
+
+Enhancement: (website: https://llm-enhance.github.io/)
+- [arXiv'23] [EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus](https://arxiv.org/abs/2307.11760). Cheng Li, Jindong Wang, Kaijie Zhu, Yixuan Zhang, Wenxin Hou, Jianxun Lian, Xing Xie.
+- [**IJCV'23**] [Exploring Vision-Language Models for Imbalanced Learning](https://arxiv.org/abs/2304.01457). Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang. [[code](https://github.com/Imbalance-VLM/Imbalance-VLM)]
+- **[IEEE Data Engineering Bulletin'23]** FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning. Wang Lu, Xixu Hu, Jindong Wang, Xing Xie. [[arxiv](https://arxiv.org/abs/2302.13485)]
+
+Open source:
   - [Project SearchAnything](https://github.com/Immortalise/SearchAnything): semantic local search.