I-I (iDEA-iSAIL) reading 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 reading group is to educate and inform its members of the recent advances of machine learning and data mining.
Time : 9:00am - 10:00am CDT, every Tuesday.
Room : 2124 Siebel
Zoom (if online) : https://illinois.zoom.us/j/6602062914?pwd=dGxWd1BKMit4b0pEcVdQc0pZTG8xZz09
Unless otherwise notified, our reading group for Fall 2023 is scheduled as follows. If you would like to present in an upcoming meeting, please submit a pull request for registering or email Dongqi Fu (dongqif2 [at] illinois [dot] edu).
Presenters, (1) please do not forget do not forget
Schedule for Spring 2024:
Dates
Presenters
Topics
Materials
Jan 09, 2024
Lihui Liu
Job Talk Dry Run
Jan 16, 2024
Xiao Lin
Anomaly Detection on Multivariate Time Series
slides
Jan 23, 2024
Xinyu He
Denoising Rec Sys
slides
Jan 30, 2024
Dongqi Fu
Job Talk Dry Run
Feb 06, 2024
Lecheng Zheng
Job Talk Dry Run
Feb 13, 2024
Jun Wu
Job Talk Dry Run
Feb 20, 2024
Dongqi Fu
Job Talk Dry Run
Feb 27, 2024
Wenxuan Bao, Tianxin Wei, Jun Wu, Yunzhe Qi, Zhichen Zeng
NeurIPS Debriefing
slides
Mar 05, 2024
Tianxin Wei, Zihao Li
NeurIPS Debriefing
slides (Zihao) Slides (Tianxin)
Mar 12, 2024
Yikun Ban
Large Language Models for Recommendation
slides
Mar 19, 2024
Wenxuan Bao, Tianxin Wei, Jun Wu, Yunzhe Qi, Zhichen Zeng
NeurIPS Debriefing
Mar 26, 2024
Wenxuan Bao, Tianxin Wei, Jun Wu, Yunzhe Qi, Zhichen Zeng
NeurIPS Debriefing
Apr 09, 2024
Zhe Xu
Preliminary Exam Dry
Apr 16, 2024
Ruizhong Qiu
Zeroth-Order Gradient Estimation
Slides
Apr 23, 2024
Ishika Agarwal
Apr 30, 2024
Hyunsik Yoo
May 07, 2024
Maggie Wu
Dates
Presenters
Topics
Materials
Aug 24, 2023
All members
Annual Lab Workshop
Recording
Aug 31, 2023
Tianxin Wei, Wenxuan Bao, Zhichen Zeng
ICML Conference Debriefing
slides
Sep 07, 2023
Ruizhong Qiu, Zhe Xu
KDD Conference Debriefing
slides
Sep 14, 2023
Zihao Wang
Query knowledge graph with learning
slides
Sep 21, 2023
Yunzhe Qi, Lihui Liu, Jun Wu
KDD Conference Debriefing (Cont.)
slides
Sep 28, 2023
Jun-Gi Jang
Efficient Tensor Decomposition
slides
Oct 05, 2023
Dongqi Fu
Preliminary Exam Dry Run
Oct 12, 2023
Zhichen Zeng
Generative Graph Dictionary Learning
slides
Oct 19, 2023
Zhining Liu
Learning from Skewed Data
slides
Oct 26, 2023
Yuchen Yan
NeurIPS Dryrun
Nov 02, 2023
Zhe Xu
Diffusion Generative Model
slides
Nov 09, 2023
Lecheng Zheng
Preliminary Exam Dry Run
Nov 16, 2023
Jun Wu
Job Talk Dry Run
Nov 30, 2023
Tianxin Wei
Schedule for Spring 2023:
Dates
Presenters
Topics
Materials
Jan 19, 2023
Professors
Heterogeneous Data Fusion
Jan 26, 2023
Chao Pan
Graph Unlearning
Speaker Info
Feb 02, 2023
Hyunsik Yoo
Out-of-Distribution Generalized Directed Network Embedding
Slides
Feb 09, 2023
Ruike Zhu
Online Graph Dictionary Learning
Slides
Feb 16, 2023
Dongqi Fu
WSDM Tutorial Dry Run
Slides
Feb 23, 2023
Zhe Xu
WSDM Tutorial Dry Run
Slides
Mar 02, 2023
Ruizhong Qiu
Meta Solver for Combinatorial Optimization Problems
Slides
Mar 09, 2023
Jian Kang
Job Talk Dry Run
Mar 23, 2023
Eunice Chan
Fair Active Learning
Slides
Mar 30, 2023
Blaine Hill / Xinrui He
HDCA for RL
Slides
Apr 06, 2023
Lecheng Zheng
SDM Paper Dry Run
Apr 13, 2023
Lecheng Zheng
SDM Tutorial Dry Run
Apr 20, 2023
Alex Zheng
Apr 27, 2023
Wenxuan Bao
Fully Test-Time Adaptation
Slides
May 04, 2023
Qinghai Zhou
May 11, 2023
Yunzhe Qi
Dates
Presenters
Topics
Materials
Aug 25, 2022
All members
Ice-breaking
Sep 01, 2022
Jian Kang
Machine Unlearning on Graphs
Slides
Sep 08, 2022
Hyunsik Yoo
Directed Network Embedding with Virtual Negative Edges
Slides
Sep 15, 2022
Jun Wu
CIKM Dry Run
Sep 22, 2022
Lecheng Zheng
CIKM Dry Run
Sep 29, 2022
Derek Wang
Source Localization of Graph Diffusion
Slides
Oct 06, 2022
Yuchen Yan
CIKM Dry Run
Oct 13, 2022
Zhichen Zeng
Fused Gromov-Wasserstein Barycenter
Slides
Oct 20, 2022
Zhe Xu
Generalized Few-Shot Node Classification
Slides
Oct 27, 2022
Yian Wang
Shift-Robust GNNs
Slides
Nov 03, 2022
Isaac Joy
Intersection Between Consumer Law and Artificial Intelligence
Slides
Nov 10, 2022
Jun Wu
Preliminary Dry Run
Nov 17, 2022
Yikun Ban
Preliminary Dry Run
Dec 01, 2022
Ishika Agarwal
Green Deep Learning
Survey
Schedule for Summer 2022:
Dates
Presenters
Topics
Materials
Jun 08 (Wed), 2022
Ziwei Wu
FAccT Dry Run
Jun 13 (Mon), 2022
Jun Wu
IJCAI Dry Run
Jun 15 (Wed), 2022
Prof. Yuan Yao' s Group
KDD Dry Run
Jun 20 (Mon), 2022
Lihui Liu
KDD Dry Run
Jun 22 (Wed), 2022
Dongqi Fu
KDD Dry Run
Jun 27 (Mon), 2022
Lecheng Zheng
KDD Dry Run
Jun 29 (Wed), 2022
Haoran Li
KDD Dry Run
Jul 11 (Mon), 2022
Jun Wu
KDD Dry Run
Jul 13 (Wed), 2022
Tianxin Wei
KDD Dry Run
Jul 18 (Mon), 2022
Yunzhe Qi
KDD Dry Run
Jul 20 (Wed), 2022
Qinghai Zhou
KDD Dry Run
Jul 25 (Mon), 2022
Jian Kang
KDD Tutorial Dry Run
Jul 27 (Wed), 2022
Jian Kang
KDD Tutorial Dry Run
Aug 08 (Mon), 2022
Jian Kang
KDD Dry Run
Dates
Presenters
Topics
Materials
Jan 24, 2022
Yuheng Zhang
AAAI Dry Run
slides
Jan 31, 2022
Tianwen Chen
Two-sided fairness in rankings via Lorenz dominance
paper
Feb 21, 2022
Lihui Liu
knowledge graph reasoning
paper
Feb 28, 2022
Baoyu Jing
Clustering Meets Contrastive Learning
slides
Mar 07, 2022
Weikai Xu
Inductive Knowledge Graph Embedding
slides
Mar 14, 2022
Yian Wang
Minimax Pareto Fairness: A Multi Objective Perspective
slides
Mar 21, 2022
Yuchen Yan
A Principle for Negative Sampling in Graph-based Recommendations
slides
Mar 28, 2022
Jian Kang, Bolian Li
WWW Dry Run
Apr 04, 2022
Shengyu Feng, Zhe Xu
WWW Dry Run
Apr 11, 2022
Derek Wang
Path Based Methods for Link Prediction
slides
Apr 18, 2022
Yikun Ban
Neural Active Learning with Performance Guarantee
slides
Apr 25, 2022
Jian Kang
Preliminary exam dry run
Apr 26, 2022
Qinghai Zhou
Preliminary exam dry run
May 02, 2022
Wenxuan Bao
Federated Learning with Knowledge Distillation
slides
May 09, 2022
Lecheng Zheng
Partial Label Learning
slides
Dates
Presenters
Topics
Materials
Sep 27, 2021
Dongqi Fu
Discovering Graph Laws and their Applications in Dynamic Graphs
Slides
Oct 13, 2021
Dawei Zhou
Thesis Defense Dry Run
Oct 18, 2021
Si Zhang
Thesis Defense Dry Run
Oct 25, 2021
Jian Kang
CIKM Dry Run
Nov 01, 2021
Zhichen Zeng
Graph Optimal Transition Coupling
Slides
Nov 08, 2021
Qinghai Zhou
Filtration Curves for Graph Classification
Slides
Nov 15, 2021
Ziwei Wu
Accuracy Parity in Group Shifts
Slides
Nov 22, 2021
Yikun Ban
Recent Advances in Neural Bandits
Slides
Nov 29, 2021
Yunzhe Qi
Introduction of Analyzing Over-parameterized Neural Networks
Slides
Dec 06, 2021
Yao Zhou
Thesis Defense Dry Run
Dec 07, 2021
Boxin Du
Thesis Defense Dry Run
Schedule for Summer 2021:
Dates
Presenters
Topics
Materials
Jun 16, 2021
Jun Wu, Lihui Liu
KDD Dry Run
Jun 18, 2021
Yikun Ban, Yao Zhou
KDD Dry Run
Jun 21, 2021
Boxin Du, Si Zhang
KDD Dry Run
Jun 23, 2021
Lihui Liu
KDD Dry Run
Jun 28, 2021
Tianxin Wei
KDD Dry Run
July 5, 2021
Dawei Zhou
Hunting Faculty Jobs in a Tight Market
July 12, 2021
Yao Zhou, Xu Liu
Industry Job Search
July 19, 2021
Si Zhang, Boxin Du
Hacking Return Offers from Industry Research Labs
July 26, 2021
Shengyu Feng
Graph Optimal Transport
Slides
Aug 2, 2021
Jun Wu
Mixup
Slides
Aug 9, 2021
Boxin Du, Yuchen Yan
Tutorial Dry Run
Aug 10, 2021
Boxin Du, Yuchen Yan
Tutorial Dry Run
Aug 23, 2021
Zhe Xu
Graph Neural Networks with Heterophily
Slides
Aug 30, 2021
Prof. Liping Liu Guest Talk about Graph Generation
Sep 06, 2021
Lecheng Zheng
Mutual Information
slides
Schedule for Spring 2021:
Dates
Presenters
Topics
Materials
Feb 22, 2021
Lecheng Zheng
Contrastive Learning
SupCon ,SimCLR , CPC , MOCO
Mar 1, 2021
Wenxuan Bao
Robustness on Federated Learning
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent , Slides
Mar 8, 2021
Jian Kang
Neural Tangent Kernel
Slides
Mar 15, 2021
Yuchen Yan
Positional Embedding and Structural Embedding in Graphs
Position Aware GNN
Mar 22, 2021
Lecheng Zheng,
WWW Dry Run
Mar 29, 2021
Yikun Ban, Haonan Wang
WWW Dry Run
Apr 5, 2021
Qinghai, Baoyu
WWW Dry Run
Apr 12, 2021
Boxin Du
Preliminary Exam Dryrun
Apr 19, 2021
Dongqi Fu
De-Oversmoothing in GNNs
PREDICT THEN PROPAGATE , PAIRNORM
Apr 26, 2021
Yuheng Zhang
Deep Q-learning and Improvements
Rainbow , Deep Q-Network , Slides
May 3, 2021
Shweta Jain
Degree Distribution Approximation
SADDLES
May 10, 2021
Jun Wu
Knowledge Distillation
1 , 2 , Slides
May 17, 2021
Lihui Liu
Knowledge Graph Embedding
1 , 2
Dates
Presenters
Topics
Materials
Sept 7, 2020
Max Welling （IAS Talk）
Graph Nets: The Next Generation
Sept 14, 2020
Yikun Ban
Online learning/ Bandits
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
Sept 21, 2020
Shengyu Feng
Graph Contrastive Learning
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training , slides
Sept 28, 2020
Lihui Liu
Neural subgraph counting
Neural subgraph isomorphism counting , slides
Oct 5, 2020
Yao Zhou
Preliminary exam dry run
Preliminary exam dry run
Oct 12, 2020
Jun Wu
Pre-Training
Using Pre-Training Can Improve Model Robustness and Uncertainty , slides
Oct 19, 2020
Ziwei Wu
Sampling Strategy in Graph
Understanding Negative Sampling in Graph Representation Learning
Oct 26, 2020
Dawei Zhou
Preliminary exam dry run
Preliminary exam dry run
Nov 2, 2020
Haonan Wang
GMNN: Graph Markov Neural Networks
GMNN: Graph Markov Neural Networks , slides
Nov 9, 2020
Lecheng Zheng
Self-supervised Learning
Multi-label Contrastive Predictive Coding , slides
Nov 16, 2020
Dongqi Fu
Fair Spectral Clustering
Guarantees for Spectral Clustering with Fairness Constraints
Nov 23, 2020
Zhe Xu
Transferring robustness
Transferring robustness for graph neural network against poisoning attacks , slides
Nov 30, 2020
Si Zhang
Preliminary exam dry run
Preliminary exam dry run
Dec 7, 2020
Qinghai Zhou
Active Learning on Graphs
Graph Policy Network for Transferable Active Learning on Graphs , slides
Dec 14, 2020
Boxin Du
Box Embedding for KBC
BoxE: A Box Embedding Model for Knowledge Base Completion , slides
Dec 15, 2020
Shweta Jain
Counting cliques in real-world graphs
Slides
Schedule for Spring 2020:
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
Multi-level spectral approach for graph embedding
GraphZoom
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
-
-
-
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)
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
Eric Wong, J. Zico Kolter:
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. ICML 2018: 5283-5292.
Chelsea Finn, Pieter Abbeel, Sergey Levine: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017: 1126-1135.
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.
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
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent