I-I Reading Group

I-I (iDEA-iSAIL) group is a statistical learning and data mining reading group at UIUC, coordinated by Prof. Hanghang Tong and Prof. Jingrui He. The main purpose of this group is to educate and inform its members of the recent advances of machine learning and data mining.

Regular Meetting Time: {Wed 11 am} at {Zoom:https://illinois.zoom.us/j/5469522866}.

Unless otherwise notified, our regular weekly meeting for Fall 2019 is Wed 11-12am, SC 4403. If you would like to present in an upcoming meeting, please email dzhou21/lecheng4 [at] illinois [dot] edu or submit a pull request and add to the table!

Schedule for 2020 Fall:

Dates	Presenters	Topics	Materials
Sept 9, 2020
Sept 16, 2020
Sept 23, 2020
Sept 30, 2020
Oct 7, 2020
Oct 14, 2020
Oct 21, 2020
Oct 28, 2020
Nov 4, 2020
Nov 11, 2020
Nov 18, 2020
Nov 25, 2020
Dec 2, 2020
Dec 9, 2020
Dec 16, 2020

Schedule for 2020 Spring:

Dates	Presenters	Topics	Materials
Mar 18, 2020	Yuchen Yan	GAN for graphs	GraphGAN, CommunityGAN
Mar 25, 2020	AAAI20	Turing Award Winners Event	Lecture by Geoffrey Hinton, Yann LeCun, Yoshua Bengio
Apr 1, 2020	Jian Kang	Graph Neural Tangent Kernel (GNTK)	Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Apr 8, 2020	Dawei Zhou, Yao Zhou	Dry run for The Web Conference 2020	-
Apr 15, 2020	Lecheng Zheng	Self supervised Learning	Representation Learning with Contrastive Predictive Coding
Apr 22, 2020	Boxin Du	-	-
Apr 29, 2020	Xu Liu	GCN with syntactic and semantic information	SynGCN
May 6, 2020	Qinghai Zhou	Learning Transferable Graph Exploration	paper
May 13, 2020	-	-	-

Recommended Flows

Introduce 1~2 Research Papers:

• 20 mins: Introduction & Background (Motivation examples, literature review)
• 10 min: Problem Description (Give a formal definition of the studied problems)
• 30 min: Brainstorm Discussion (Propose potential approaches based on your knowledge)
• 30 min: Algorithm (Description of the algorithms in the papers)
• 30 min: Critical Discussion (Pros & Cons of your ideas and the existing one)

Survey a Research Topic

• 20 mins: Introduction & Background (Motivation examples, literature review)
• 20 min: Problem/Subproblems Description (Give a formal definition of the studied problems)
• 60 min: Review (High-level discussion of the existing work)
• 20 min: Conclusion & Future Direction

Covered topics/papers in the past:

Generative Deep Learning:

• Martín Arjovsky, Soumith Chintala, Léon Bottou: Wasserstein Generative Adversarial Networks. ICML 2017: 214-223 
• Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, Aaron C. Courville: Improved Training of Wasserstein GANs. NIPS 2017: 5767-5777 
• You, Jiaxuan, et al. "Graphrnn: Generating realistic graphs with deep auto-regressive models." arXiv preprint arXiv:1802.08773 (2018). 
• Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann: NetGAN: Generating Graphs via Random Walks. ICML 2018: 609-618 

Robustness:

• Eric Wong, J. Zico Kolter: Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. ICML 2018: 5283-5292.  

Meta Learning:

• Chelsea Finn, Pieter Abbeel, Sergey Levine: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017: 1126-1135. 

Fairness Learning:

• Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Tauman Kalai: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. NIPS 2016: 4349-4357.  
• Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, Cynthia Dwork: Learning Fair Representations. ICML (3) 2013: 325-333.  

Adversarial Attacks:

• Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song: Adversarial Attack on Graph Structured Data. ICML 2018: 1123-1132 . 
• Daniel Zügner, Amir Akbarnejad, Stephan Günnemann: Adversarial Attacks on Neural Networks for Graph Data. KDD 2018: 2847-2856. 
• Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang: Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples. NeurIPS 2018: 7728-7739 

Tracking PageRank vector:

• Andersen, Reid, Fan Chung, and Kevin Lang. "Local graph partitioning using pagerank vectors." 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06). IEEE, 2006. 
• Ohsaka, Naoto, Takanori Maehara, and Ken-ichi Kawarabayashi. "Efficient pagerank tracking in evolving networks." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. 
• Zhang, Hongyang, Peter Lofgren, and Ashish Goel. "Approximate personalized pagerank on dynamic graphs." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. 

Click to see what we have covered in each semester