Li-Ping Liu

According to our database, Li-Ping Liu authored at least 40 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets.

Panagiotis Lymperopoulos, Liping Liu

CoRR, 2024

2023

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.

Vladimir Porokhin, Li-Ping Liu, Soha Hassoun

Bioinform., March, 2023

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds.

Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes

Trans. Mach. Learn. Res., 2023

Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation.

Xu Han, Xiaohui Chen, Francisco J. R. Ruiz, Li-Ping Liu

J. Mach. Learn. Res., 2023

Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis.

Xiaohui Chen, Yongfei Liu, Yingxiang Yang, Jianbo Yuan, Quanzeng You, Li-Ping Liu, Hongxia Yang

CoRR, 2023

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation.

Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, Jian-Xun Wang

CoRR, 2023

EDGE++: Improved Training and Sampling of EDGE.

Mingyang Wu, Xiaohui Chen, Liping Liu

CoRR, 2023

Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases.

Thomas Schnelldorfer, Janil Castro, Atoussa Goldar-Najafi, Liping Liu

CoRR, 2023

Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model.

Luning Sun, Xu Han, Han Gao, Jian-Xun Wang, Liping Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Separate Normalization in Self-supervised Transformers.

Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Liping Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems.

Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling.

Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu

Proceedings of the International Conference on Machine Learning, 2023

2022

Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning.

Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu

Trans. Mach. Learn. Res., 2022

Interpretable Node Representation with Attribute Decoding.

Xiaohui Chen, Xi Chen, Li-Ping Liu

Trans. Mach. Learn. Res., 2022

NVDiff: Graph Generation through the Diffusion of Node Vectors.

Xiaohui Chen, Yukun Li, Aonan Zhang, Li-Ping Liu

CoRR, 2022

Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation.

Xinmeng Li, Hao Zhu, Li-Ping Liu, Soha Hassoun

CoRR, 2022

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction.

Xinmeng Li, Li-Ping Liu, Soha Hassoun

Bioinform., 2022

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations.

Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu

Proceedings of the Learning on Graphs Conference, 2022

Predicting Physics in Mesh-reduced Space with Temporal Attention.

Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Liping Liu

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Modeling Graph Node Correlations with Neighbor Mixture Models.

Linfeng Liu, Michael C. Hughes, Liping Liu

CoRR, 2021

Learning graph representations of biochemical networks and its application to enzymatic link prediction.

Julie Jiang, Li-Ping Liu, Soha Hassoun

Bioinform., 2021

Stochastic Iterative Graph Matching.

Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu

Proceedings of the 38th International Conference on Machine Learning, 2021

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation.

Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu

Proceedings of the 38th International Conference on Machine Learning, 2021

GAN Ensemble for Anomaly Detection.

Xu Han, Xiaohui Chen, Li-Ping Liu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Using Graph Neural Networks for Mass Spectrometry Prediction.

Hao Zhu, Li-Ping Liu, Soha Hassoun

CoRR, 2020

Localizing and Amortizing: Efficient Inference for Gaussian Processes.

Linfeng Liu, Liping Liu

Proceedings of The 12th Asian Conference on Machine Learning, 2020

Kriging Convolutional Networks.

Gabriel Appleby, Linfeng Liu, Liping Liu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling.

Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu, Soha Hassoun

CoRR, 2019

Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes.

Linfeng Liu, Liping Liu

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes.

Linfeng Liu, Liping Liu

CoRR, 2018

2017

Context Selection for Embedding Models.

Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Transductive Optimization of Top k Precision.

Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Bayesian Active Clustering with Pairwise Constraints.

Yuanli Pei, Li-Ping Liu, Xiaoli Z. Fern

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

2014

Gaussian Approximation of Collective Graphical Models.

Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich

Proceedings of the 31th International Conference on Machine Learning, 2014

Learnability of the Superset Label Learning Problem.

Li-Ping Liu, Thomas G. Dietterich

Proceedings of the 31th International Conference on Machine Learning, 2014

2012

Constructing Training Sets for Outlier Detection.

Li-Ping Liu, Xiaoli Z. Fern

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

A Conditional Multinomial Mixture Model for Superset Label Learning.

Li-Ping Liu, Thomas G. Dietterich

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Incorporating Boosted Regression Trees into Ecological Latent Variable Models.

Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2009

Least Square Incremental Linear Discriminant Analysis.

Li-Ping Liu, Yuan Jiang, Zhi-Hua Zhou

Proceedings of the ICDM 2009, 2009

2008

TEFE: A Time-Efficient Approach to Feature Extraction.

Li-Ping Liu, Yang Yu, Yuan Jiang, Zhi-Hua Zhou

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008