Liu, Liping

30 publications

ICML 2025 Graph Generative Pre-Trained Transformer Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Liping Liu

ICLRW 2025 Graph Generative Pre-Trained Transformer Xiaohui Chen, Yinkai Wang, Jiaxing He, Yuanqi Du, Soha Hassoun, Xiaolin Xu, Liping Liu

TMLR 2025 Graph-Based Confidence Calibration for Large Language Models Yukun Li, Sijia Wang, Lifu Huang, Liping Liu

ICLRW 2025 Large Language Model Is Secretly a Protein Sequence Optimizer Yinkai Wang, Jiaxing He, Yuanqi Du, Xiaohui Chen, Jianan Canal Li, Liping Liu, Xiaolin Xu, Soha Hassoun

ICLR 2025 MADGEN: Mass-Spec Attends to De Novo Molecular Generation Yinkai Wang, Xiaohui Chen, Liping Liu, Soha Hassoun

AISTATS 2024 Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets Panagiotis Lymperopoulos, Liping Liu

TMLR 2024 Incorporating Inductive Biases to Energy-Based Generative Models Yukun Li, Liping Liu

NeurIPSW 2023 EDGE++: Improved Training and Sampling of EDGE Xiaohui Chen, Mingyang Wu, Liping Liu

NeurIPSW 2023 EDGE++: Improved Training and Sampling of EDGE Xiaohui Chen, Mingyang Wu, Liping Liu

ICML 2023 Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu

TMLR 2023 NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds Cynthia Feeney, Sarah Schneider, Panagiotis Lymperopoulos, Liping Liu, Matthias Scheutz, Michael C Hughes

NeurIPS 2023 On Separate Normalization in Self-Supervised Transformers Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Liping Liu

NeurIPS 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

NeurIPSW 2022 Exploiting Variable Correlation with Masked Modeling for Anomaly Detection in Time Series Panagiotis Lymperopoulos, Yukun Li, Liping Liu

TMLR 2022 Interpretable Node Representation with Attribute Decoding Xiaohui Chen, Xi Chen, Liping Liu

LoG 2022 PatchGT: Transformer over Non-Trainable Clusters for Learning Graph Representations Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu

ICLR 2022 Predicting Physics in Mesh-Reduced Space with Temporal Attention Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, Liping Liu

TMLR 2022 Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning Linfeng Liu, Xu Han, Dawei Zhou, Liping Liu

ICML 2021 Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation Xiaohui Chen, Xu Han, Jiajing Hu, Francisco Ruiz, Liping Liu

ICML 2021 Stochastic Iterative Graph Matching Linfeng Liu, Michael C Hughes, Soha Hassoun, Liping Liu

AAAI 2020 Kriging Convolutional Networks Gabriel Appleby, Linfeng Liu, Liping Liu

ACML 2020 Localizing and Amortizing: Efficient Inference for Gaussian Processes Linfeng Liu, Liping Liu

AISTATS 2019 Amortized Variational Inference with Graph Convolutional Networks for Gaussian Processes Linfeng Liu, Liping Liu

ICLR 2019 Delta: Deep Learning Transfer Using Feature mAP with Attention for Convolutional Networks Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, Jun Huan

NeurIPS 2017 Context Selection for Embedding Models Liping Liu, Francisco Ruiz, Susan Athey, David Blei

ICML 2014 Gaussian Approximation of Collective Graphical Models Liping Liu, Daniel Sheldon, Thomas Dietterich

ICML 2014 Learnability of the Superset Label Learning Problem Liping Liu, Thomas Dietterich

NeurIPS 2012 A Conditional Multinomial Mixture Model for Superset Label Learning Liping Liu, Thomas G. Dietterich

UAI 2003 A Linear Belief Function Approach to Portfolio Evaluation Liping Liu, Catherine Shenoy, Prakash P. Shenoy

AISTATS 1995 Propagation of Gaussian Belief Functions Liping Liu