The photo below is me and my wife hiking at the Glacier National Park. We have done many hiking, offroading, and other outdoor activities across the U.S. Among the many destinations, Utah, Montana, and Alaska are my favoriate destinations so far. My wife find Big Island most attractive, I can’t argue with her because I also enjoy the snorkeling expeirence and the resorts. 
Photography isn’t exactly my hobbie – I engage in it to add an extra elemet of joy to my outdoors activities, and give my wife some motivations to join me. An ‘amateur photograher’ is is how I title myself when explaining to other hikers why I carry the expensive gears on the trail. However, one thing that I am always hesitant to disclose to others is that I earn a living through machine learning … 
Published in Thesis, 2018
Da Xu (2018). "Symmetric Tensor Regression for Functional Connectivity Analysis." Thesis. https://arxiv.org/pdf/2010.14700.pdf
Published in Statistics in Biosciences, 2018
Xiaoshan Li, Da Xu, et.al (2018). "Tucker Tensor Regression and Neuroimaging Analysis; Statistics in Biosciences. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336908/
Published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Da Xu, Chuanwei Ruan, et.al (2019). "Generative Graph Convolutional Network for Growing Graphs." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://arxiv.org/pdf/1903.02640.pdf
Published in Neural Information Processing Systems Conference (NeurIPS), 2019
Da Xu, Chuanwei Ruan, et.al (2019). "Self-attention with Functional Time Representation Learning." Neural Information Processing Systems Conference (NeurIPS). https://proceedings.neurips.cc/paper/2019/file/cf34645d98a7630e2bcca98b3e29c8f2-Paper.pdf
Published in ACM Internation Conference on Web Search and Data Mining (WSDM), 2020
Da Xu, Chuanwei Ruan, et.al (2020). "Knowledge-aware Complementary Product Representation Learning." ACM Internation Conference on Web Search and Data Mining (WSDM). https://arxiv.org/pdf/1904.12574.pdf
Published in ACM Internation Conference on Web Search and Data Mining (WSDM), 2020
Da Xu, Chuanwei Ruan, et.al (2020). "Product Knowledge Graph Embedding for E-commerce." ACM Internation Conference on Web Search and Data Mining (WSDM). https://arxiv.org/pdf/1911.12481.pdf
Published in International Conference on Learning Representations (ICLR), 2020
Da Xu, Chuanwei Ruan, et.al (2020). "Inductive representation learning on temporal graphs." International Conference on Learning Representations (ICLR). https://arxiv.org/pdf/2002.07962.pdf
Published in Neural Information Processing Systems Conference (NeruIPS), 2020
Da Xu, Chuanwei Ruan, et.al (2020). "Adversarial Counterfactual Learning and Evaluation for Recommender Systems." Neural Information Processing Systems Conference (NeruIPS). https://proceedings.neurips.cc/paper/2020/file/9cd013fe250ebffc853b386569ab18c0-Paper.pdf
Published in ACM Internation Conference on Web Search and Data Mining (WSDM), 2021
Da Xu, et.al (2021). "Theoretical Understandings of Product Embedding for E-commerce Machine Learning." ACM Internation Conference on Web Search and Data Mining (WSDM). https://arxiv.org/pdf/2102.12029.pdf
Published in 2nd Workshop of Industrial Recommender System, KDD, 2021
Cheng Jie, Da Xu, et.al (2021). "Bidding via Clustering Ads Intentions: an Efficient Search Engine Marketing System for E-commerce." 2nd Workshop of Industrial Recommender System, KDD. https://www.researchgate.net/profile/Cheng-Jie/publication/353568822_Bidding_via_Clustering_Ads_Intentions_an_Efficient_Search_Engine_Marketing_System_for_E-Commerce/links/610376dc1e95fe241a98f691/Bidding-via-Clustering-Ads-Intentions-an-Efficient-Search-Engine-Marketing-System-for-E-Commerce.pdf
Published in International Conference on Learning Representations (ICLR), 2021
Da Xu, Yuting Ye, Chuanwei Ruan (2021). "Understanding the Role of Importance Weighting for Deep Learning." International Conference on Learning Representations (ICLR). https://arxiv.org/pdf/2103.15209.pdf
Published in International Conference of Machine Learning (ICML), 2021
Da Xu, Chaunwei Ruan, et.al (2021). "Rethinking Neural v.s. Matrix Factorization Collaborative Filtering: the Theoretical Prespectives,." International Conference of Machine Learning (ICML). http://proceedings.mlr.press/v139/xu21d/xu21d.pdf
Published in International Conference on Learning Representations (ICLR), 2021
Da Xu et.al (2021). "A Temporal Kernel Approach for Deep Learning with Continuous-time Information,." International Conference on Learning Representations (ICLR). https://arxiv.org/pdf/2103.15213.pdf
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021
Da Xu, Chaunwei Ruan, et.al (2021). "Towards the D-optimal Experiment Design for Online Recommender Selection." ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). https://arxiv.org/pdf/2110.12132.pdf
Published in International Conference on Learning Representations (ICLR), 2022
Da Xu, Yuting Ye, et.al (2022). "From Intervention to Domain Transportation: a Novel Perspective to Optimize Recommendation." International Conference on Learning Representations (ICLR). https://arxiv.org/pdf/2203.13956.pdf
Published in AAAI conference on Artificial Intelligence, 2022
Da Xu, Yuting Ye, et.al (2022). "Towards Robust Off-policy Learning for Online Uncertain." AAAI conference on Artificial Intelligence. https://arxiv.org/pdf/2202.13337.pdf
Published in Arxiv, 2022
Xu S, Xu D, Korpeoglu E, et al. Causal Structure Learning with Recommendation System[J]. arXiv preprint arXiv:2210.10256, 2022 https://arxiv.org/abs/2210.10256
Published in Workshop of Decision Making for Modern Information Retrieval (WSDM), 2022
Da Xu, et.al (2022). "On the Advances and Challenges for Adaptive Online Testing." Workshop of Decision Making for Modern Information Retrieval (WSDM). https://arxiv.org/pdf/2203.07672.pdf
Published:
Industrial tutorial, Walmart Labs, 2022
In the past several years, I spent most of my time productionizing recommender systems and researching the various gaps between what we observe in practice and what existing theory tells. Modern recommender system, in my experience, has evolved a long way from the initial Netflix challenge and collaborative filtering approaches. The same story happens for Information Retrieval (IR) as well, though it seems the boudary between IR and Recsys is gradually fading these days – many shared ideas and solutions are binding these two fields, and I believe this trend will continue in the future. In general, the most exciting advancement in the past decade has to do with deep learning (DL), and in one way or another, Recsys seems to be adopting it more than IR. Please don’t get me wrong, embracing DL does not mean significant progress has been made in the field, to me it just means more questions and challenges ahead. (Not finished…)