TEFE: A Time-Efficient Approach to Feature Extraction. - Li-Ping Liu 001; Yang Yu 001; Yuan Jiang 001; Zhi-Hua Zhou (ICDM, 2008)

Least Square Incremental Linear Discriminant Analysis. - Li-Ping Liu 001; Yuan Jiang 001; Zhi-Hua Zhou (ICDM, 2009)

Incorporating Boosted Regression Trees into Ecological Latent Variable Models. - Rebecca A. Hutchinson; Li-Ping Liu 001; Thomas G. Dietterich (AAAI, 2011)

A Conditional Multinomial Mixture Model for Superset Label Learning. - Li-Ping Liu 001; Thomas G. Dietterich (NIPS, 2012)

Constructing Training Sets for Outlier Detection. - Li-Ping Liu 001; Xiaoli Z. Fern (SDM, 2012)

Learnability of the Superset Label Learning Problem. - Li-Ping Liu 001; Thomas G. Dietterich (ICML, 2014)

Gaussian Approximation of Collective Graphical Models. - Li-Ping Liu 001; Daniel Sheldon; Thomas G. Dietterich (ICML, 2014)

Bayesian Active Clustering with Pairwise Constraints. - Yuanli Pei; Li-Ping Liu 001; Xiaoli Z. Fern (ECML/PKDD (1), 2015)

Transductive Optimization of Top k Precision. - Li-Ping Liu 001; Thomas G. Dietterich; Nan Li 019; Zhi-Hua Zhou (IJCAI, 2016)

Context Selection for Embedding Models. - Li-Ping Liu 001; Francisco J. R. Ruiz; Susan Athey; David M. Blei (NIPS, 2017)

Non-Parametric Variational Inference with Graph Convolutional Networks for Gaussian Processes. - Linfeng Liu 004; Liping Liu 001 (CoRR, 2018)

Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes. - Linfeng Liu 004; Liping Liu 001 (AISTATS, 2019)

Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling. - Ramtin Hosseini; Neda Hassanpour; Li-Ping Liu 001; Soha Hassoun (CoRR, 2019)

Kriging Convolutional Networks. - Gabriel Appleby; Linfeng Liu 004; Liping Liu 001 (AAAI, 2020)

Localizing and Amortizing: Efficient Inference for Gaussian Processes. - Linfeng Liu 004; Liping Liu 001 (ACML, 2020)

Using Graph Neural Networks for Mass Spectrometry Prediction. - Hao Zhu; Li-Ping Liu 001; Soha Hassoun (CoRR, 2020)

GAN Ensemble for Anomaly Detection. - Xu Han 012; Xiaohui Chen; Li-Ping Liu 001 (AAAI, 2021)

Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation. - Xiaohui Chen; Xu Han 012; Jiajing Hu; Francisco J. R. Ruiz; Li-Ping Liu 001 (ICML, 2021)

Stochastic Iterative Graph Matching. - Linfeng Liu 004; Michael C. Hughes; Soha Hassoun; Liping Liu 001 (ICML, 2021)

Learning graph representations of biochemical networks and its application to enzymatic link prediction. - Julie Jiang; Li-Ping Liu 001; Soha Hassoun (Bioinform., 2021)

Modeling Graph Node Correlations with Neighbor Mixture Models. - Linfeng Liu 004; Michael C. Hughes; Liping Liu 001 (CoRR, 2021)

Predicting Physics in Mesh-reduced Space with Temporal Attention. - Xu Han 012; Han Gao 005; Tobias Pfaff; Jian-Xun Wang 001; Liping Liu 001 (ICLR, 2022)

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. - Han Gao 005; Xu Han 012; Jiaoyang Huang; Jian-Xun Wang 001; Liping Liu 001 (LoG, 2022)

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction. - Xinmeng Li; Li-Ping Liu 001; Soha Hassoun (Bioinform., 2022)

Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation. - Xinmeng Li; Hao Zhu; Li-Ping Liu 001; Soha Hassoun (CoRR, 2022)

NVDiff: Graph Generation through the Diffusion of Node Vectors. - Xiaohui Chen; Yukun Li; Aonan Zhang; Li-Ping Liu 001 (CoRR, 2022)

Interpretable Node Representation with Attribute Decoding. - Xiaohui Chen; Xi Chen; Li-Ping Liu 001 (Trans. Mach. Learn. Res., 2022)

Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning. - Linfeng Liu 004; Xu Han 012; Dawei Zhou; Li-Ping Liu 001 (Trans. Mach. Learn. Res., 2022)

Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. - Xiaohui Chen; Jiaxing He; Xu Han 012; Liping Liu 001 (ICML, 2023)

Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems. - Xiaohui Chen; Jiankai Sun; Taiqing Wang; Ruocheng Guo; Li-Ping Liu 001; Aonan Zhang (KDD, 2023)

On Separate Normalization in Self-supervised Transformers. - Xiaohui Chen; Yinkai Wang; Yuanqi Du; Soha Hassoun; Liping Liu 001 (NeurIPS, 2023)

Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model. - Luning Sun; Xu Han 012; Han Gao 005; Jian-Xun Wang 001; Liping Liu 001 (NeurIPS, 2023)

Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases. - Thomas Schnelldorfer; Janil Castro; Atoussa Goldar-Najafi; Liping Liu 001 (CoRR, 2023)

EDGE++: Improved Training and Sampling of EDGE. - Mingyang Wu; Xiaohui Chen; Liping Liu 001 (CoRR, 2023)

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. - Han Gao 005; Xu Han 012; Xiantao Fan; Luning Sun; Li-Ping Liu 001; Lian Duan; Jian-Xun Wang 001 (CoRR, 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 001; Hongxia Yang (CoRR, 2023)

Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation. - Xu Han 012; Xiaohui Chen; Francisco J. R. Ruiz; Li-Ping Liu 001 (J. Mach. Learn. Res., 2023)

NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds. - Patrick Feeney; Sarah Schneider; Panagiotis Lymperopoulos; Liping Liu 001; Matthias Scheutz; Michael C. Hughes (Trans. Mach. Learn. Res., 2023)

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products. - Vladimir Porokhin; Li-Ping Liu 001; Soha Hassoun (Bioinform., 2023)

Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets. - Panagiotis Lymperopoulos; Liping Liu 001 (AISTATS, 2024)

MassSpecGym: A benchmark for the discovery and identification of molecules. - Roman Bushuiev; Anton Bushuiev; Niek F. de Jonge; Adamo Young; Fleming Kretschmer; Raman Samusevich; Janne Heirman; Fei Wang 062; Luke Zhang; Kai Dührkop; Marcus Ludwig; Nils A. Haupt; et al. (NeurIPS, 2024)

A neurosymbolic cognitive architecture framework for handling novelties in open worlds. - 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 001; et al. (Artif. Intell., 2024)

Graph Generative Pre-trained Transformer. - Xiaohui Chen; Yinkai Wang; Jiaxing He; Yuanqi Du; Soha Hassoun; Xiaolin Xu; Li-Ping Liu 001 (CoRR, 2025)

Large Language Model is Secretly a Protein Sequence Optimizer. - Yinkai Wang; Jiaxing He; Yuanqi Du; Xiaohui Chen; Jianan Canal Li; Liping Liu 001; Xiaolin Xu; Soha Hassoun (CoRR, 2025)