Li-Ping Liu

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 22

Top Topics

Graph Representations

Probabilistic Modeling

Diffusion Models

Top Venues

Trans. Mach. Learn. Res.

Publications

Shivam Goel, Panagiotis Lymperopoulos, Ravenna Thielstrom, Evan A. Krause, Patrick Feeney, Pierrick Lorang, Sarah Schneider, Yichen Wei, Eric J. Kildebeck, Stephen A. Goss, Michael C. Hughes, Li-Ping Liu, Jivko Sinapov, Matthias Scheutz
A neurosymbolic cognitive architecture framework for handling novelties in open worlds. Artif. Intell. 331 (2024)
Gabriel Appleby, Linfeng Liu, Li-Ping Liu
Kriging Convolutional Networks. CoRR (2023)
Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Li-Ping Liu
On Separate Normalization in Self-supervised Transformers. CoRR (2023)
Vladimir Porokhin, Li-Ping Liu, Soha Hassoun
Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products. Bioinform. 39 (3) (2023)
Xiaohui Chen, Jiaxing He, Xu Han, Li-Ping Liu
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. CoRR (2023)
Xiaohui Chen, Yongfei Liu, Yingxiang Yang, Jianbo Yuan, Quanzeng You, Li-Ping Liu, Hongxia Yang
Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis. CoRR (2023)
Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu
Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation. J. Mach. Learn. Res. 24 (2023)
Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. KDD (2023)
Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, Jian-Xun Wang
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. CoRR (2023)
Xiaohui Chen, Yukun Li, Aonan Zhang, Li-Ping Liu
NVDiff: Graph Generation through the Diffusion of Node Vectors. CoRR (2022)
Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. Trans. Mach. Learn. Res. 2022 (2022)
Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Li-Ping Liu
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations. CoRR (2022)
Xinmeng Li, Li-Ping Liu, Soha Hassoun
Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction. Bioinform. 38 (10) (2022)
Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. CoRR (2022)
Xinmeng Li, Hao Zhu, Li-Ping Liu, Soha Hassoun
Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation. CoRR (2022)
Xiaohui Chen, Xi Chen, Li-Ping Liu
Interpretable Node Representation with Attribute Decoding. Trans. Mach. Learn. Res. 2022 (2022)
Xinmeng Li, Li-Ping Liu, Soha Hassoun
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction. CoRR (2021)
Julie Jiang, Li-Ping Liu, Soha Hassoun
Learning graph representations of biochemical networks and its application to enzymatic link prediction. Bioinform. 37 (6) (2021)
Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu
Stochastic Iterative Graph Matching. CoRR (2021)
Hao Zhu, Li-Ping Liu, Soha Hassoun
Using Graph Neural Networks for Mass Spectrometry Prediction. CoRR (2020)
Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu, Soha Hassoun
Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling. CoRR (2019)
Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou
Transductive Optimization of Top k Precision. IJCAI (2016)
Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou
Transductive Optimization of Top k Precision. CoRR (2015)
Yuanli Pei, Li-Ping Liu, Xiaoli Z. Fern
Bayesian Active Clustering with Pairwise Constraints. ECML/PKDD (1) (2015)
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
Gaussian Approximation of Collective Graphical Models. CoRR (2014)
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
Gaussian Approximation of Collective Graphical Models. ICML (2014)
Li-Ping Liu, Thomas G. Dietterich
Learnability of the Superset Label Learning Problem. ICML (2014)
Li-Ping Liu, Thomas G. Dietterich
A Conditional Multinomial Mixture Model for Superset Label Learning. NIPS (2012)
Li-Ping Liu, Xiaoli Z. Fern
Constructing Training Sets for Outlier Detection. SDM (2012)
Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich
Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI (2011)
Li-Ping Liu, Yuan Jiang, Zhi-Hua Zhou
Least Square Incremental Linear Discriminant Analysis. ICDM (2009)
Li-Ping Liu, Yang Yu, Yuan Jiang, Zhi-Hua Zhou
TEFE: A Time-Efficient Approach to Feature Extraction. ICDM (2008)