I will be presenting! Come say hi

Another great opportunity for me to present the NeurIPS Conference #NeurIPS2023 work of Rachael Sim together with my former PhD student @nghiaht87 (now an assistant professor at Washington State University).

Excited to present the joint work with Lam Chi Thanh (Steve) and my advisors Chuan-Sheng Foo & Bryan Kian Hsiang Low!

Starting my 1st day as a visiting scholar UC Berkeley EECS, hosted and advised by Prof. Michael I. Jordan.

Happy to share the release of the book "Federated Learning: Theory and Practice" that I co-edited with Lam M. Nguyen @nghiaht87, covering fundamentals, emerging topics, and applications. Kudos to the amazing contributors to make this book happen! Elsevier News Elsevier | ScienceDirect

They serve as good introductory readings in the above-mentioned topics on #FederatedLearning. More recent works on these topics can be found on our research group webpage: comp.nus.edu.sg/~lowkh/researc…. A shout-out to Lin Xiaoqiang Xinyi Xu Wu Zhaoxuan Rachael Sim ... (2/n).

📢: The 114th FLOW talk is on Wednesday (6th March) at **5 pm UTC**. Xinyi Xu (NUS) will discuss "Fairness and Incentives for Data Sharing and Collaborative Learning." Please register to our mailing list: bit.ly/3WVplLU.

👏👏👏 Angel Rachael Sim Fan Jue Xiao Tian @asdasd266463941 Zhuanghua Liu Lin Xiaoqiang Xinyi Xu Wu Zhaoxuan Rui Qiao Dai Zhongxiang @ZCODE Hu.Wenyang patrick jaillet ICML Conference #ICML2024 #ShapleyValue #DataValuation #DataCentricAI #LLMs #BayesianOptimization #ExplainableAI

The ICML Conference #ICML2024 work of Lin Xiaoqiang Xinyi Xu Wu Zhaoxuan et Al. presents distributionally robust #DataValuation without a known validation distribution. #DataCentricAI Paper: openreview.net/forum?id=mbBeh… Visit us at Poster Session 3 Wed 24 Jul 11:30AM Hall C 4-9 #2402

I will be ICML Conference in Vienna and presenting our work. Happy to chat!

For #InContextLearning, how do we attribute an #LLM prediction to each task demonstration? 🚀 Zijian Zhou Lin Xiaoqiang Xinyi Xu et al. introduces DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning in ICML Conference #ICML2024 Workshop on #ICL (1/n)

GLOW.AI will be at ICML Conference #ICML2024: Xinyi Xu Wu Zhaoxuan Rachael Sim Rui Qiao Apivich H. (Kaotoo) Lin Xiaoqiang Angel Zhiliang Chen Zhuanghua Liu Gregory Lau Xinyuan. See u soon!

The #EMNLP2024 EMNLP 2025 (findings) position paper of Xinyi Xu Wu Zhaoxuan Rui Qiao Arun Verma Pang Wei Koh et al. proposes a data-centric viewpoint of AI research, focusing on #LLM #LLMs. Check it out @ arxiv.org/abs/2406.14473

Big shout out and congratulations to my collaborators Giulia Fanti and Shuaiqi Wang for our paper during my visit at CMU!

Obligatory first week tweet: Very excited to start as a research scientist intern with the Central Applied Science team Meta in Menlo Park. Looking forward to connecting with old and new friends! Ping me if you are also in Bay area for coffee, or hikes! #swag #firstweek

Xinyuan and Gregory Lau presenting our position paper on data-centric AI in the age of #LLM EMNLP 2025 #EMNLP2024! Thanks to Xinyi Xu for preparing the poster! Joint work with Pang Wei Koh.

👏👏👏 pham nguyen Rachael Sim qphong Zhiliang Chen Xinyuan Lin Xiaoqiang Xinyi Xu patrick jaillet Dai Zhongxiang Arun Verma Rui Qiao Wu Zhaoxuan Angel Pang Wei Koh Apivich H. (Kaotoo) Gregory Lau Bingchen Wang et al. Accepted ICLR 2026 #ICLR2025 AAAI #AAAI2025

🚀 Tired of slow, expensive data selection for training LLMs? Still using #ShapleyValues or #InfluenceFunctions to find high-quality data, and burning through compute? 😩🔥💸 🌟 The new ICLR 2025 work of Lin Xiaoqiang Xinyi Xu on Efficient Top-m Data Values

Chuan Sheng Foo and I are proud to graduate our 2nd jointly supervised PhD student, Xinyi Xu!!! His PhD thesis topic is on “Fair Data Valuation for Collaborative Machine Learning”. We had our farewell dinner at a restaurant in Singapore where he introduced us to the

Inspired by the success of inverse problems in uncovering fundamental scientific laws, our position paper, which is accepted to EMNLP 2025 #EMNLP2025 (findings), argues that inverse problems can be used to efficiently uncover underlying scaling laws for #LLMs that help the