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ORCIDLi-Ping Liu 0001
Person information
- affiliation: Tufts University, Medford, MA, USA
- affiliation: Oregon State University, School of Electrical Engineering and Computer Science, Corvallis, USA
- affiliation: Nanjing University, National Key Laboratory for Novel Software Technology, China
Other persons with the same name
- Li-Ping Liu (aka: Liping Liu) — disambiguation page
- Liping Liu 0002
![[0000-0001-5133-8923]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-5133-8923")
— Southeast University, School of Economics and Management, Nanjing, China - Liping Liu 0003 (aka: LiPing Liu 0003) — Beijing Institute of Technology, Computer department, China
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2020 – today
- 2024
[i26]Panagiotis Lymperopoulos, Liping Liu:
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. CoRR abs/2402.15524 (2024)- 2023
- [j7]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) - [j6]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: 97:1-97:30 (2023) - [j5]Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes:
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. Trans. Mach. Learn. Res. 2023 (2023) - [c22]Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu:
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. ICML 2023: 4585-4610 - [c21]Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang:
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. KDD 2023: 3865-3876 - [c20]Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Liping Liu:
On Separate Normalization in Self-supervised Transformers. NeurIPS 2023 - [c19]Luning Sun, Xu Han, Han Gao, Jian-Xun Wang, Liping Liu:
Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model. NeurIPS 2023 - [i25]Xiaohui Chen, Jiaxing He, Xu Han, Li-Ping Liu:
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. CoRR abs/2305.04111 (2023) - [i24]Xiaohui Chen, Jiankai Sun, Taiqing Wang, Ruocheng Guo, Li-Ping Liu, Aonan Zhang:
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. CoRR abs/2305.16391 (2023) - [i23]Gabriel Appleby, Linfeng Liu, Li-Ping Liu:
Kriging Convolutional Networks. CoRR abs/2306.09463 (2023) - [i22]Thomas Schnelldorfer, Janil Castro, Atoussa Goldar-Najafi, Liping Liu:
Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases. CoRR abs/2306.10380 (2023) - [i21]Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Li-Ping Liu:
On Separate Normalization in Self-supervised Transformers. CoRR abs/2309.12931 (2023) - [i20]Mingyang Wu, Xiaohui Chen, Liping Liu:
EDGE++: Improved Training and Sampling of EDGE. CoRR abs/2310.14441 (2023) - [i19]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 abs/2311.07896 (2023) - [i18]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 abs/2311.17126 (2023) - 2022
- [j4]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): 2832-2838 (2022) - [j3]Xiaohui Chen, Xi Chen, Li-Ping Liu:
Interpretable Node Representation with Attribute Decoding. Trans. Mach. Learn. Res. 2022 (2022) - [j2]Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu:
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. Trans. Mach. Learn. Res. 2022 (2022) - [c18]Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Liping Liu:
Predicting Physics in Mesh-reduced Space with Temporal Attention. ICLR 2022 - [c17]Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu:
PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. LoG 2022: 27 - [i17]Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Li-Ping Liu:
Predicting Physics in Mesh-reduced Space with Temporal Attention. CoRR abs/2201.09113 (2022) - [i16]Xinmeng Li, Hao Zhu, Li-Ping Liu, Soha Hassoun:
Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation. CoRR abs/2203.13783 (2022) - [i15]Patrick Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C. Hughes:
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. CoRR abs/2206.11736 (2022) - [i14]Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu:
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. CoRR abs/2210.10643 (2022) - [i13]Xiaohui Chen, Yukun Li, Aonan Zhang, Li-Ping Liu:
NVDiff: Graph Generation through the Diffusion of Node Vectors. CoRR abs/2211.10794 (2022) - [i12]Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Li-Ping Liu:
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations. CoRR abs/2211.14425 (2022) - [i11]Xiaohui Chen, Xi Chen, Liping Liu:
Interpretable Node Representation with Attribute Decoding. CoRR abs/2212.01682 (2022) - 2021
- [j1]Julie Jiang, Li-Ping Liu, Soha Hassoun:
Learning graph representations of biochemical networks and its application to enzymatic link prediction. Bioinform. 37(6): 793-799 (2021) - [c16]Xu Han, Xiaohui Chen, Li-Ping Liu:
GAN Ensemble for Anomaly Detection. AAAI 2021: 4090-4097 - [c15]Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu:
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. ICML 2021: 1630-1639 - [c14]Linfeng Liu, Michael C. Hughes, Soha Hassoun, Liping Liu:
Stochastic Iterative Graph Matching. ICML 2021: 6815-6825 - [i10]Linfeng Liu, Michael C. Hughes, Liping Liu:
Modeling Graph Node Correlations with Neighbor Mixture Models. CoRR abs/2103.15966 (2021) - [i9]Linfeng Liu, Michael C. Hughes, Soha Hassoun, Li-Ping Liu:
Stochastic Iterative Graph Matching. CoRR abs/2106.02206 (2021) - [i8]Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Li-Ping Liu:
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. CoRR abs/2106.06189 (2021) - [i7]Xinmeng Li, Li-Ping Liu, Soha Hassoun:
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction. CoRR abs/2109.14766 (2021) - 2020
- [c13]Gabriel Appleby, Linfeng Liu, Liping Liu:
Kriging Convolutional Networks. AAAI 2020: 3187-3194 - [c12]Linfeng Liu, Liping Liu:
Localizing and Amortizing: Efficient Inference for Gaussian Processes. ACML 2020: 823-836 - [i6]Hao Zhu, Li-Ping Liu, Soha Hassoun:
Using Graph Neural Networks for Mass Spectrometry Prediction. CoRR abs/2010.04661 (2020) - [i5]Xu Han, Xiaohui Chen, Li-Ping Liu:
GAN Ensemble for Anomaly Detection. CoRR abs/2012.07988 (2020)
2010 – 2019
- 2019
- [c11]Linfeng Liu, Liping Liu:
Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. AISTATS 2019: 2291-2300 - [i4]Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu, Soha Hassoun:
Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling. CoRR abs/1912.05753 (2019) - 2018
- [i3]Linfeng Liu, Liping Liu:
Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes. CoRR abs/1809.02838 (2018) - 2017
- [c10]Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Context Selection for Embedding Models. NIPS 2017: 4816-4825 - 2016
- [c9]Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou:
Transductive Optimization of Top k Precision. IJCAI 2016: 1781-1787 - 2015
- [c8]Yuanli Pei, Li-Ping Liu, Xiaoli Z. Fern:
Bayesian Active Clustering with Pairwise Constraints. ECML/PKDD (1) 2015: 235-250 - [i2]Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou:
Transductive Optimization of Top k Precision. CoRR abs/1510.05976 (2015) - 2014
- [c7]Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. ICML 2014: 1602-1610 - [c6]Li-Ping Liu, Thomas G. Dietterich:
Learnability of the Superset Label Learning Problem. ICML 2014: 1629-1637 - [i1]Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich:
Gaussian Approximation of Collective Graphical Models. CoRR abs/1405.5156 (2014) - 2012
- [c5]Li-Ping Liu, Thomas G. Dietterich:
A Conditional Multinomial Mixture Model for Superset Label Learning. NIPS 2012: 557-565 - [c4]Li-Ping Liu, Xiaoli Z. Fern:
Constructing Training Sets for Outlier Detection. SDM 2012: 919-929 - 2011
- [c3]Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Dietterich:
Incorporating Boosted Regression Trees into Ecological Latent Variable Models. AAAI 2011: 1343-1348
2000 – 2009
- 2009
- [c2]Li-Ping Liu, Yuan Jiang, Zhi-Hua Zhou:
Least Square Incremental Linear Discriminant Analysis. ICDM 2009: 298-306 - 2008
- [c1]Li-Ping Liu, Yang Yu, Yuan Jiang, Zhi-Hua Zhou:
TEFE: A Time-Efficient Approach to Feature Extraction. ICDM 2008: 423-432
Coauthor Index
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last updated on 2024-05-03 19:57 CEST by the dblp team
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