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ORCIDLiping Liu 0001
Person information
- affiliation: Tufts University, Department of Computer Science, Medford, MA, USA
- affiliation: Oregon State University, School of Electrical Engineering and Computer Science, Corvallis, OR, 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.org/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
[j8]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: 104111 (2024)- [c23]Panagiotis Lymperopoulos, Liping Liu:
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. AISTATS 2024: 2647-2655 - [i27]Panagiotis Lymperopoulos, Liping Liu:
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. CoRR abs/2402.15524 (2024) - [i26]Roman Bushuiev, Anton Bushuiev, Niek F. de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, Marcus Ludwig, Nils A. Haupt, Apurva Kalia, Corinna Brungs, Robin Schmid, Russell Greiner, Bo Wang, David S. Wishart, Li-Ping Liu, Juho Rousu, Wout Bittremieux, Hannes Rost, Tytus D. Mak, Soha Hassoun, Florian Huber, Justin J. J. van der Hooft, Michael A. Stravs, Sebastian Böcker, Josef Sivic, Tomás Pluskal:
MassSpecGym: A benchmark for the discovery and identification of molecules. CoRR abs/2410.23326 (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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