2023/08: Start my Postdoc at GaTech in Atlanta.
2023/05: Our paper "Representer Point Selection for Explaining Regularized High-dimensional Models" is accepted by ICML 2023 [arXiv]! Big congrats to Che-Ping who leads this project!
2023/05: Our paper "PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation" is accepted by ICML 2023! [arXiv, code]
2023/01: Our paper "Unlearning Nonlinear Graph Classifiers in the Limited Training Data Regime" is accepted by TheWebConf 2023 (365/1900, 19.2%. a.k.a. WWW)! [arXiv, code]
2023/01: Our paper "Efficient Model Updates for Approximate Unlearning of Graph-Structured Data" is accepted by ICLR 2023! Check our previous NeurIPS Workshop version [arXiv, code] and stay tuned for the camera-ready version!
2022/11: Our journal paper "Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces" has been accepted by KAIS! This is a journal extension of our previous ICDM paper about theories of linear classifications in hyperbolic spaces. You may find our draft on [arXiv].
2022/11: I passed my Ph.D. final exam!
2022/10: Our paper "Certified Graph Unlearning" will be presented in NeurIPS 2022 GLFrontiers Workshop! You may check our draft on [arXiv] and stay tuned for a refined camera-ready version.
2022/05: I start my applied scientist internship at Amazon again!
2022/05: Our paper "HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering" has been accepted by KDD 2022 (research track) as a full paper! (254/1695, 15%)
2022/04: Our paper "Small-sample estimation of the mutational support and distribution of SARS-CoV-2" has been accepted by TCBB Journal (IEEE/ACM Transactions on Computational Biology and Bioinformatics)! Big congrats to Vishal who leads this project! You can find an earlier version of it here [medRxiv].
2022/01: Two papers got accepted by ICLR 2022!
"Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction" We improve general graph learning tasks with raw data (such as text)!
"You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks" We propose an unified framework for hypergraph neural networks, with connection to deep learning in set functions such as DeepSet and SetTransformer!
2021/11: Our paper "Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction" has achieved rank-1 with huge improvement in performance on 3 datasets of OGB leaderboard!
2021/09: Our paper "Highly Scalable and Provably Accurate Classification in Poincare Balls" has been accepted by ICDM 2021 as a regular paper! (98/990, 9.9%)
2021/08: I receive Amazon Post Internship Fellowship according to my internship performance this summer! Big thanks to the help from my collaborators.
2021/08: Our paper "Landing Probabilities of Random Walks for Seed-Set Expansion in Hypergraphs" has been accepted by ITW 2021!
2021/06: I start my applied scientist internship at Amazon!
2021/04: Our paper "Support Estimation with Sampling Artifacts and Errors" has been accepted by ISIT 2021!
2021/01: Our paper "Adaptive Universal Generalized PageRank Graph Neural Network" has been accepted by ICLR 2021! Our code can be found at github.
2020/12: I gave a talk on my recent researches "Learning on graphs: from algorithmics approaches to graph neural networks" at National Taiwan University! Main references: GPR and GPR-GNN.
2020/12: Personal website launch.