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 Thursday.
Room: TBD
Zoom (if online): https://illinois.zoom.us/j/6602062914?pwd=dGxWd1BKMit4b0pEcVdQc0pZTG8xZz09
Unless otherwise notified, our reading group for Fall 2024 is scheduled as follows. If you would like to present in an upcoming meeting, please edit the README.md and submit a pull request for registering.
Presenters, (1) please do not forget to upload your nice presentation slides to this github repository; (2) please also do not forget to forward the papers you are going to represent a week ahead of your presentation.
| Dates | Presenters | Topics | Materials |
|---|---|---|---|
| Sep 05, 2024 | Hyunsik Yoo, Xinrui He, et al | Web 2024 Debriefing | Slides |
| Sep 12, 2024 | Yikun, Ruizhong, Wenxuan, Zhichen | Rebuttal Panel: Dos and Don'ts | Slides |
| Sep 19, 2024 | Zhining Liu, Zhichen Zeng | ICML 2024 Debriefing | Slides |
| Sep 26, 2024 | Yikun Ban, Lecheng Zheng | KDD 2024 Debriefing | |
| Oct 03, 2024 | Ruizhong Qiu, Zihao Li | ICML 2024 Debriefing | Score Entropy LLM honesty |
| Oct 10, 2024 | Xiao Lin | State Space Model and Mamba | Slides |
| Oct 17, 2024 | Xinyu He | ||
| Oct 24, 2024 | Tianxin Wei | ||
| Oct 31, 2024 | Lecheng Zheng | ||
| Nov 07, 2024 | Ting-Wei Li | Machine Unlearning w/ Data Attribution | https://github.com/isail-laboratory/iDEA-iSAIL-Reading-Group/blob/master/slides/20241107.pdf |
| Nov 14, 2024 | Zhe Xu | RAG | Slides |
| Nov 21, 2024 | |||
| Dec 05, 2024 | Jiaru Zou |
| 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) recording (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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 | - | - | - |
- 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
- 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
- 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.