Discriminant kernel and regularization parameter learning via semidefinite programming. - Jieping Ye; Jianhui Chen; Shuiwang Ji (ICML, 2007)

Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming. - Jieping Ye; Shuiwang Ji; Jianhui Chen (KDD, 2007)

A unified framework for generalized Linear Discriminant Analysis. - Shuiwang Ji; Jieping Ye (CVPR, 2008)

A least squares formulation for canonical correlation analysis. - Liang Sun 001; Shuiwang Ji; Jieping Ye (ICML, 2008)

Learning subspace kernels for classification. - Jianhui Chen; Shuiwang Ji; Betul Ceran; Qi Li 001; Mingrui Wu; Jieping Ye (KDD, 2008)

Extracting shared subspace for multi-label classification. - Shuiwang Ji; Lei Tang 001; Shipeng Yu; Jieping Ye (KDD, 2008)

Hypergraph spectral learning for multi-label classification. - Liang Sun 001; Shuiwang Ji; Jieping Ye (KDD, 2008)

Multi-label Multiple Kernel Learning. - Shuiwang Ji; Liang Sun 001; Rong Jin 001; Jieping Ye (NIPS, 2008)

Automated annotation of Drosophila gene expression patterns using a controlled vocabulary. - Shuiwang Ji; Liang Sun 001; Rong Jin 001; Sudhir Kumar 001; Jieping Ye (Bioinform., 2008)

Adaptive diffusion kernel learning from biological networks for protein function prediction. - Liang Sun 001; Shuiwang Ji; Jieping Ye (BMC Bioinform., 2008)

Multi-class Discriminant Kernel Learning via Convex Programming. - Jieping Ye; Shuiwang Ji; Jianhui Chen (J. Mach. Learn. Res., 2008)

Kernel Uncorrelated and Regularized Discriminant Analysis: A Theoretical and Computational Study. - Shuiwang Ji; Jieping Ye (IEEE Trans. Knowl. Data Eng., 2008)

Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection. - Shuiwang Ji; Jieping Ye (IEEE Trans. Neural Networks, 2008)

An accelerated gradient method for trace norm minimization. - Shuiwang Ji; Jieping Ye (ICML, 2009)

A least squares formulation for a class of generalized eigenvalue problems in machine learning. - Liang Sun 001; Shuiwang Ji; Jieping Ye (ICML, 2009)

Linear Dimensionality Reduction for Multi-label Classification. - Shuiwang Ji; Jieping Ye (IJCAI, 2009)

DrosophilaGene Expression Pattern Annotation through Multi-Instance Multi-Label Learning. - Ying-Xin Li; Shuiwang Ji; Sudhir Kumar 001; Jieping Ye; Zhi-Hua Zhou (IJCAI, 2009)

On the Equivalence between Canonical Correlation Analysis and Orthonormalized Partial Least Squares. - Liang Sun 001; Shuiwang Ji; Shipeng Yu; Jieping Ye (IJCAI, 2009)

Drosophila gene expression pattern annotation using sparse features and term-term interactions. - Shuiwang Ji; Lei Yuan 001; Ying-Xin Li; Zhi-Hua Zhou; Sudhir Kumar 001; Jieping Ye (KDD, 2009)

Mining discrete patterns via binary matrix factorization. - Bao-Hong Shen; Shuiwang Ji; Jieping Ye (KDD, 2009)

Detecting Human Actions in Surveillance Videos. - Ming Yang 007; Shuiwang Ji; Wei Xu 007; Jinjun Wang; Fengjun Lv; Kai Yu 001; Yihong Gong; Mert Dikmen; Dennis J. Lin; Thomas S. Huang (TRECVID, 2009)

Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization. - Jun Liu 003; Shuiwang Ji; Jieping Ye (UAI, 2009)

A bag-of-words approach for Drosophila gene expression pattern annotation. - Shuiwang Ji; Ying-Xin Li; Zhi-Hua Zhou; Sudhir Kumar 001; Jieping Ye (BMC Bioinform., 2009)

Trace Norm Regularization: Reformulations, Algorithms, and Multi-Task Learning. - Ting Kei Pong; Paul Tseng; Shuiwang Ji; Jieping Ye (SIAM J. Optim., 2010)

A shared-subspace learning framework for multi-label classification. - Shuiwang Ji; Lei Tang 001; Shipeng Yu; Jieping Ye (ACM Trans. Knowl. Discov. Data, 2010)

Computational network analysis of the anatomical and genetic organizations in the mouse brain. - Shuiwang Ji (Bioinform., 2011)

FlyExpress: visual mining of spatiotemporal patterns for genes and publications in Drosophila embryogenesis. - Sudhir Kumar 001; Charlotte Konikoff; Bernard Van Emden; Christopher Busick; Kailah T. Davis; Shuiwang Ji; Lin-Wei Wu; Hector Ramos; Thomas Brody; Sethuraman Panchanathan; Jieping Ye; Timothy L. Karr; et al. (Bioinform., 2011)

Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis. - Liang Sun 001; Shuiwang Ji; Jieping Ye (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

A sparsity-inducing formulation for evolutionary co-clustering. - Shuiwang Ji; Wenlu Zhang; Jun Liu 003 (KDD, 2012)

Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval. - Lei Yuan 001; Alexander Woodard; Shuiwang Ji; Yuan Jiang 001; Zhi-Hua Zhou; Sudhir Kumar 001; Jieping Ye (BMC Bioinform., 2012)

Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization - Jun Liu 003; Shuiwang Ji; Jieping Ye (CoRR, 2012)

Discriminant sparse neighborhood preserving embedding for face recognition. - Jie Gui; Zhenan Sun; Wei Jia 001; Rong-Xiang Hu; Ying-Ke Lei; Shuiwang Ji (Pattern Recognit., 2012)

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning. - Ying-Xin Li; Shuiwang Ji; Sudhir Kumar 001; Jieping Ye; Zhi-Hua Zhou (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Evolutionary Soft Co-Clustering. - Shuiwang Ji; Wenlu Zhang; Rui Zhang (SDM, 2013)

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering. - Shuiwang Ji (BMC Bioinform., 2013)

Image-level and group-level models for Drosophila gene expression pattern annotation. - Qian Sun 002; Sherin Muckatira; Lei Yuan 001; Shuiwang Ji; Stuart J. Newfeld; Sudhir Kumar 001; Jieping Ye (BMC Bioinform., 2013)

A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis. - Wenlu Zhang; Daming Feng; Rongjian Li; Andrey N. Chernikov; Nikos Chrisochoides; Christopher Osgood; Charlotte Konikoff; Stuart J. Newfeld; Sudhir Kumar 001; Shuiwang Ji (BMC Bioinform., 2013)

3D Convolutional Neural Networks for Human Action Recognition. - Shuiwang Ji; Wei Xu 007; Ming Yang 007; Kai Yu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

A Probabilistic Latent Semantic Analysis Model for Coclustering the Mouse Brain Atlas. - Shuiwang Ji; Wenlu Zhang; Rongjian Li (IEEE ACM Trans. Comput. Biol. Bioinform., 2013)

Multiview Partitioning via Tensor Methods. - Xinhai Liu; Shuiwang Ji; Wolfgang Glänzel; Bart De Moor (IEEE Trans. Knowl. Data Eng., 2013)

Robust Deep Learning for Improved Classification of AD/MCI Patients. - Feng Li 039; Loc Tran; Kim-Han Thung; Shuiwang Ji; Dinggang Shen; Jiang Li 001 (MLMI, 2014)

Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis. - Rongjian Li; Wenlu Zhang; Heung-Il Suk; Li Wang 026; Jiang Li 001; Dinggang Shen; Shuiwang Ji (MICCAI (3), 2014)

Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression. - Lei Yuan 001; Cheng Pan; Shuiwang Ji; Michael McCutchan; Zhi-Hua Zhou; Stuart J. Newfeld; Sudhir Kumar 001; Jieping Ye (Bioinform., 2014)

Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns. - Rongjian Li; Wenlu Zhang; Shuiwang Ji (BMC Bioinform., 2014)

Integrative analysis of the connectivity and gene expression atlases in the mouse brain. - Shuiwang Ji; Ahmed Fakhry; Houtao Deng (NeuroImage, 2014)

How to Estimate the Regularization Parameter for Spectral Regression Discriminant Analysis and its Kernel Version? - Jie Gui; Zhenan Sun; Jun Cheng; Shuiwang Ji; Xindong Wu 001 (IEEE Trans. Circuits Syst. Video Technol., 2014)

Deep Convolutional Neural Networks for Multi-instance Multi-task Learning. - Tao Zeng; Shuiwang Ji (ICDM, 2015)

Structural Graphical Lasso for Learning Mouse Brain Connectivity. - Sen Yang 004; Qian Sun 002; Shuiwang Ji; Peter Wonka; Ian Davidson; Jieping Ye (KDD, 2015)

Automated Gene Expression Pattern Annotation in the Mouse Brain. - Tao Yang 016; Xinlin Zhao; Binbin Lin; Tao Zeng; Shuiwang Ji; Jieping Ye (Pacific Symposium on Biocomputing, 2015)

Deep convolutional neural networks for annotating gene expression patterns in the mouse brain. - Tao Zeng; Rongjian Li; Ravi Mukkamala; Jieping Ye; Shuiwang Ji (BMC Bioinform., 2015)

Global analysis of gene expression and projection target correlations in the mouse brain. - Ahmed Fakhry; Tao Zeng; Hanchuan Peng; Shuiwang Ji (Brain Informatics, 2015)

Evolutionary soft co-clustering: formulations, algorithms, and applications. - Wenlu Zhang; Rongjian Li; Daming Feng; Andrey N. Chernikov; Nikos Chrisochoides; Christopher Osgood; Shuiwang Ji (Data Min. Knowl. Discov., 2015)

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. - Wenlu Zhang; Rongjian Li; Houtao Deng; Li Wang 026; Weili Lin; Shuiwang Ji; Dinggang Shen (NeuroImage, 2015)

A Robust Deep Model for Improved Classification of AD/MCI Patients. - Feng Li 039; Loc Tran; Kim-Han Thung; Shuiwang Ji; Dinggang Shen; Jiang Li 001 (IEEE J. Biomed. Health Informatics, 2015)

Sparsity Learning Formulations for Mining Time-Varying Data. - Rongjian Li; Wenlu Zhang; Yao Zhao 001; Zhenfeng Zhu; Shuiwang Ji (IEEE Trans. Knowl. Data Eng., 2015)

Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy. - Rongjian Li; Dong Si; Tao Zeng; Shuiwang Ji; Jing He 002 (BIBM, 2016)

Parallel Lasso Screening for Big Data Optimization. - Qingyang Li 001; Shuang Qiu; Shuiwang Ji; Paul M. Thompson; Jieping Ye; Jie Wang 005 (KDD, 2016)

Multi-Task Feature Interaction Learning. - Kaixiang Lin; Jianpeng Xu; Inci M. Baytas; Shuiwang Ji; Jiayu Zhou (KDD, 2016)

Collaborative Multi-View Denoising. - Lei Zhang 116; Shupeng Wang; Xiaoyu Zhang 002; Yong Wang 032; Binbin Li 001; Dinggang Shen; Shuiwang Ji (KDD, 2016)

Deep models for brain EM image segmentation: novel insights and improved performance. - Ahmed Fakhry; Hanchuan Peng; Shuiwang Ji (Bioinform., 2016)

IDM 2017: Workshop on Interpretable Data Mining - Bridging the Gap between Shallow and Deep Models. - Xia Hu 001; Shuiwang Ji (CIKM, 2017)

A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis. - Debrup Banerjee; Kazi Aminul Islam; Gang Mei; Lemin Xiao; Guangfan Zhang; Roger Xu; Shuiwang Ji; Jiang Li 001 (ICDM, 2017)

Efficient and Invariant Convolutional Neural Networks for Dense Prediction. - Hongyang Gao; Shuiwang Ji (ICDM, 2017)

Recurrent Encoder-Decoder Networks for Time-Varying Dense Prediction. - Tao Zeng; Bian Wu; Jiayu Zhou; Ian Davidson; Shuiwang Ji (ICDM, 2017)

Multi-Modality Disease Modeling via Collective Deep Matrix Factorization. - Qi Wang; Mengying Sun; Liang Zhan; Paul Thompson 001; Shuiwang Ji; Jiayu Zhou (KDD, 2017)

DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation. - Tao Zeng; Bian Wu; Shuiwang Ji (Bioinform., 2017)

Pixel Deconvolutional Networks. - Hongyang Gao; Hao Yuan 001; Zhengyang Wang; Shuiwang Ji (CoRR, 2017)

Dense Transformer Networks. - Jun Li 098; Yongjun Chen; Lei Cai; Ian Davidson; Shuiwang Ji (CoRR, 2017)

Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation. - Ahmed Fakhry; Tao Zeng; Shuiwang Ji (IEEE Trans. Medical Imaging, 2017)

Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction. - Rongjian Li; Tao Zeng; Hanchuan Peng; Shuiwang Ji (IEEE Trans. Medical Imaging, 2017)

Feature Selection Based on Structured Sparsity: A Comprehensive Study. - Jie Gui; Zhenan Sun; Shuiwang Ji; Dacheng Tao; Tieniu Tan (IEEE Trans. Neural Networks Learn. Syst., 2017)

Deep Adversarial Learning for Multi-Modality Missing Data Completion. - Lei Cai; Zhengyang Wang; Hongyang Gao; Dinggang Shen; Shuiwang Ji (KDD, 2018)

Voxel Deconvolutional Networks for 3D Brain Image Labeling. - Yongjun Chen; Hongyang Gao; Lei Cai; Min Shi; Dinggang Shen; Shuiwang Ji (KDD, 2018)

Large-Scale Learnable Graph Convolutional Networks. - Hongyang Gao; Zhengyang Wang; Shuiwang Ji (KDD, 2018)

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. - Hongyang Gao; Zhengyang Wang; Shuiwang Ji (NeurIPS, 2018)

Learning Convolutional Text Representations for Visual Question Answering. - Zhengyang Wang; Shuiwang Ji (SDM, 2018)

Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation. - Zhengyang Wang; Na Zou 001; Dinggang Shen; Shuiwang Ji (CoRR, 2018)

Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli. - Lei Cai; Bian Wu; Shuiwang Ji (Neuroinformatics, 2018)

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality. - Zhongyu Li 002; Erik Butler; Kang Li 004; Aidong Lu; Shuiwang Ji; Shaoting Zhang 001 (Neuroinformatics, 2018)

Multi-View Missing Data Completion. - Lei Zhang 116; Yao Zhao 001; Zhenfeng Zhu; Dinggang Shen; Shuiwang Ji (IEEE Trans. Knowl. Data Eng., 2018)

Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods. - Hao Yuan 001; Yongjun Chen; Xia Hu 001; Shuiwang Ji (AAAI, 2019)

An Efficient Policy Gradient Method for Conditional Dialogue Generation. - Lei Cai; Shuiwang Ji (ICDM, 2019)

Learning Local and Global Multi-context Representations for Document Classification. - Yi Liu 059; Hao Yuan 001; Shuiwang Ji (ICDM, 2019)

Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction. - Hao Yuan 001; Na Zou 001; Shaoting Zhang 001; Hanchuan Peng; Shuiwang Ji (ICDM, 2019)

Dense Transformer Networks for Brain Electron Microscopy Image Segmentation. - Jun Li 098; Yongjun Chen; Lei Cai; Ian Davidson; Shuiwang Ji (IJCAI, 2019)

Graph Representation Learning via Hard and Channel-Wise Attention Networks. - Hongyang Gao; Shuiwang Ji (KDD, 2019)

An Interpretable Neural Model with Interactive Stepwise Influence. - Yin Zhang 011; Ninghao Liu; Shuiwang Ji; James Caverlee; Xia Hu 001 (PAKDD (3), 2019)

Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation. - Lei Cai; Hongyang Gao; Shuiwang Ji (SDM, 2019)

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions. - Zhengyang Wang; Hao Yuan 001; Shuiwang Ji (SDM, 2019)

On Attribution of Recurrent Neural Network Predictions via Additive Decomposition. - Mengnan Du; Ninghao Liu; Fan Yang 023; Shuiwang Ji; Xia Hu 001 (WWW, 2019)

Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. - Hongyang Gao; Yongjun Chen; Shuiwang Ji (WWW, 2019)

XFake: Explainable Fake News Detector with Visualizations. - Fan Yang 023; Shiva K. Pentyala; Sina Mohseni; Mengnan Du; Hao Yuan 001; Rhema Linder; Eric D. Ragan; Shuiwang Ji; Xia (Ben) Hu (WWW, 2019)

Computational modeling of cellular structures using conditional deep generative networks. - Hao Yuan 001; Lei Cai; Zhengyang Wang; Xia Hu 001; Shaoting Zhang 001; Shuiwang Ji (Bioinform., 2019)

Global Transformer U-Nets for Label-Free Prediction of Fluorescence Images. - Yi Liu 059; Hao Yuan 001; Zhengyang Wang; Shuiwang Ji (CoRR, 2019)

A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. - Debrup Banerjee; Kazi Aminul Islam; Keyi Xue; Gang Mei; Lemin Xiao; Guangfan Zhang; Roger Xu; Cai Lei; Shuiwang Ji; Jiang Li 001 (Knowl. Inf. Syst., 2019)

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection. - Yujie Feng; Fan Yang 021; Xichuan Zhou; Yanli Guo; Fang Tang; Fengbo Ren; Jishun Guo; Shuiwang Ji (IEEE ACM Trans. Comput. Biol. Bioinform., 2019)

A Multi-Scale Approach for Graph Link Prediction. - Lei Cai; Shuiwang Ji (AAAI, 2020)

Adaptive Convolutional ReLUs. - Hongyang Gao; Lei Cai; Shuiwang Ji (AAAI, 2020)

Non-Local U-Nets for Biomedical Image Segmentation. - Zhengyang Wang; Na Zou 001; Dinggang Shen; Shuiwang Ji (AAAI, 2020)

Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis. - Hanzi Mao; Xi Liu 011; Nick Duffield; Hao Yuan 001; Shuiwang Ji; Binayak P. Mohanty (ICDM, 2020)

CorDEL: A Contrastive Deep Learning Approach for Entity Linkage. - Zhengyang Wang; Bunyamin Sisman; Hao Wei; Xin Luna Dong; Shuiwang Ji (ICDM, 2020)

StructPool: Structured Graph Pooling via Conditional Random Fields. - Hao Yuan 001; Shuiwang Ji (ICLR, 2020)

Kronecker Attention Networks. - Hongyang Gao; Zhengyang Wang; Shuiwang Ji (KDD, 2020)

Towards Deeper Graph Neural Networks. - Meng Liu 015; Hongyang Gao; Shuiwang Ji (KDD, 2020)

Deep Learning of High-Order Interactions for Protein Interface Prediction. - Yi Liu 059; Hao Yuan 001; Lei Cai; Shuiwang Ji (KDD, 2020)

XGNN: Towards Model-Level Explanations of Graph Neural Networks. - Hao Yuan 001; Jiliang Tang; Xia Hu 001; Shuiwang Ji (KDD, 2020)

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising. - Yaochen Xie; Zhengyang Wang; Shuiwang Ji (NeurIPS, 2020)

Deep Neural Networks with Knowledge Instillation. - Fan Yang 023; Ninghao Liu; Mengnan Du; Kaixiong Zhou; Shuiwang Ji; Xia Hu 001 (SDM, 2020)

iCapsNets: Towards Interpretable Capsule Networks for Text Classification. - Zhengyang Wang; Xia Hu 001; Shuiwang Ji (CoRR, 2020)

Augmented Equivariant Attention Networks for Electron Microscopy Image Super-Resolution. - Yaochen Xie; Yu Ding 002; Shuiwang Ji (CoRR, 2020)

MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery. - Zhengyang Wang; Meng Liu 015; Youzhi Luo; Zhao Xu 005; Yaochen Xie; Limei Wang; Lei Cai; Shuiwang Ji (CoRR, 2020)

Pixel Transposed Convolutional Networks. - Hongyang Gao; Hao Yuan 001; Zhengyang Wang; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Adversarial Attacks and Defenses on Graphs. - Wei Jin 009; Yaxin Li 001; Han Xu 002; Yiqi Wang 001; Shuiwang Ji; Charu Aggarwal 001; Jiliang Tang (SIGKDD Explor., 2020)

Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis. - Wenlu Zhang; Rongjian Li; Tao Zeng; Qian Sun 002; Sudhir Kumar 001; Jieping Ye; Shuiwang Ji (IEEE Trans. Big Data, 2020)

Global Pixel Transformers for Virtual Staining of Microscopy Images. - Yi Liu 059; Hao Yuan 001; Zhengyang Wang; Shuiwang Ji (IEEE Trans. Medical Imaging, 2020)

zhengyang-wang/GVTNets: Code for "Global Voxel Transformer Networks for Augmented Microscopy". - Zhengyang Wang; Yaochen Xie; Shuiwang Ji (Zenodo, 2020)

Graph Based Machine Learning for Healthcare: State of the Art, Challenges, and Opportunities. - Yuan Luo 001; Fei Wang 001; Marinka Zitnik; Shuiwang Ji (AMIA, 2021)

AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter. - Yushun Dong; Kaize Ding; Brian Jalaian; Shuiwang Ji; Jundong Li (CIKM, 2021)

GraphDF: A Discrete Flow Model for Molecular Graph Generation. - Youzhi Luo; Keqiang Yan; Shuiwang Ji (ICML, 2021)

On Explainability of Graph Neural Networks via Subgraph Explorations. - Hao Yuan 001; Haiyang Yu 005; Jie Wang 005; Kang Li 004; Shuiwang Ji (ICML, 2021)

Machine Learning Explanations to Prevent Overtrust in Fake News Detection. - Sina Mohseni; Fan Yang 023; Shiva K. Pentyala; Mengnan Du; Yi Liu 059; Nic Lupfer; Xia Hu 001; Shuiwang Ji; Eric D. Ragan (ICWSM, 2021)

Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence. - Qi Qi 006; Youzhi Luo; Zhao Xu 005; Shuiwang Ji; Tianbao Yang (NeurIPS, 2021)

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs. - Zhanqiu Zhang; Jie Wang 005; Jiajun Chen 001; Shuiwang Ji; Feng Wu 001 (NeurIPS, 2021)

Node2Seq: Towards Trainable Convolutions in Graph Neural Networks. - Hao Yuan 001; Shuiwang Ji (CoRR, 2021)

A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis. - Yi Liu 059; Shuiwang Ji (CoRR, 2021)

GraphEBM: Molecular Graph Generation with Energy-Based Models. - Meng Liu 015; Keqiang Yan; Bora Oztekin; Shuiwang Ji (CoRR, 2021)

Spherical Message Passing for 3D Graph Networks. - Yi Liu 059; Limei Wang; Meng Liu 015; Xuan Zhang; Bora Oztekin; Shuiwang Ji (CoRR, 2021)

Self-Supervised Learning of Graph Neural Networks: A Unified Review. - Yaochen Xie; Zhao Xu 005; Zhengyang Wang; Shuiwang Ji (CoRR, 2021)

Adversarial Graph Disentanglement. - Shuai Zheng 005; Zhenfeng Zhu; Zhizhe Liu; Shuiwang Ji; Yao Zhao 001 (CoRR, 2021)

Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence. - Hongyang Gao; Yi Liu 059; Xuan Zhang; Shuiwang Ji (CoRR, 2021)

Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence. - Qi Qi 006; Youzhi Luo; Zhao Xu 005; Shuiwang Ji; Tianbao Yang (CoRR, 2021)

Graph Neural Networks with Adaptive Frequency Response Filter. - Yushun Dong; Kaize Ding; Brian Jalaian; Shuiwang Ji; Jundong Li (CoRR, 2021)

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks. - Meng Liu 015; Cong Fu 003; Xuan Zhang; Limei Wang; Yaochen Xie; Hao Yuan 001; Youzhi Luo; Zhao Xu 005; Shenglong Xu; Shuiwang Ji (CoRR, 2021)

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs. - Zhao Xu 005; Youzhi Luo; Xuan Zhang; Xinyi Xu; Yaochen Xie; Meng Liu 015; Kaleb Dickerson; Cheng Deng; Maho Nakata; Shuiwang Ji (CoRR, 2021)

Smoothed dilated convolutions for improved dense prediction. - Zhengyang Wang; Shuiwang Ji (Data Min. Knowl. Discov., 2021)

DIG: A Turnkey Library for Diving into Graph Deep Learning Research. - Meng Liu 015; Youzhi Luo; Limei Wang; Yaochen Xie; Hao Yuan 001; Shurui Gui; Haiyang Yu 005; Zhao Xu 005; Jingtun Zhang; Yi Liu 059; Keqiang Yan; Haoran Liu; et al. (J. Mach. Learn. Res., 2021)

Global voxel transformer networks for augmented microscopy. - Zhengyang Wang; Yaochen Xie; Shuiwang Ji (Nat. Mach. Intell., 2021)

Topology-Aware Graph Pooling Networks. - Hongyang Gao; Yi Liu 059; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. - Hongyang Gao; Zhengyang Wang; Lei Cai; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy. - Yi Liu 059; Shuiwang Ji (IEEE Trans. Medical Imaging, 2021)

Spherical Message Passing for 3D Molecular Graphs. - Yi Liu 059; Limei Wang; Meng Liu 015; Yuchao Lin; Xuan Zhang; Bora Oztekin; Shuiwang Ji (ICLR, 2022)

An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch. - Youzhi Luo; Shuiwang Ji (ICLR, 2022)

Generating 3D Molecules for Target Protein Binding. - Meng Liu 015; Youzhi Luo; Kanji Uchino; Koji Maruhashi; Shuiwang Ji (ICML, 2022)

Self-Supervised Representation Learning via Latent Graph Prediction. - Yaochen Xie; Zhao Xu 005; Shuiwang Ji (ICML, 2022)

GraphFM: Improving Large-Scale GNN Training via Feature Momentum. - Haiyang Yu 005; Limei Wang; Bokun Wang; Meng Liu 015; Tianbao Yang; Shuiwang Ji (ICML, 2022)

Frontiers of Graph Neural Networks with DIG. - Shuiwang Ji; Meng Liu 015; Yi Liu 059; Youzhi Luo; Limei Wang; Yaochen Xie; Zhao Xu 005; Haiyang Yu 005 (KDD, 2022)

Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions. - Rui Yang 031; Jie Wang 005; Zijie Geng; Mingxuan Ye; Shuiwang Ji; Bin Li 025; Feng Wu 001 (KDD, 2022)

GOOD: A Graph Out-of-Distribution Benchmark. - Shurui Gui; Xiner Li; Limei Wang; Shuiwang Ji (NeurIPS, 2022)

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs. - Limei Wang; Yi Liu 059; Yuchao Lin; Haoran Liu; Shuiwang Ji (NeurIPS, 2022)

Task-Agnostic Graph Explanations. - Yaochen Xie; Sumeet Katariya; Xianfeng Tang; Edward W. Huang; Nikhil Rao 001; Karthik Subbian; Shuiwang Ji (NeurIPS, 2022)

Periodic Graph Transformers for Crystal Material Property Prediction. - Keqiang Yan; Yi Liu 059; Yuchao Lin; Shuiwang Ji (NeurIPS, 2022)

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences. - Meng Liu 015; Shuiwang Ji (SDM, 2022)

Advanced graph and sequence neural networks for molecular property prediction and drug discovery. - Zhengyang Wang; Meng Liu 015; Youzhi Luo; Zhao Xu 005; Yaochen Xie; Limei Wang; Lei Cai; Qi Qi 006; Zhuoning Yuan; Tianbao Yang; Shuiwang Ji (Bioinform., 2022)

Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks. - Meng Liu 015; Haiyang Yu 005; Shuiwang Ji (CoRR, 2022)

Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification. - Xueliang Wang; Jianyu Cai; Shuiwang Ji; Houqiang Li; Feng Wu 001; Jie Wang 005 (CoRR, 2022)

Learning Protein Representations via Complete 3D Graph Networks. - Limei Wang; Haoran Liu; Yi Liu 059; Jerry Kurtin; Shuiwang Ji (CoRR, 2022)

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding. - Haitao Lin; Yufei Huang 002; Meng Liu 015; Xuanjing Li; Shuiwang Ji; Stan Z. Li (CoRR, 2022)

Line Graph Neural Networks for Link Prediction. - Lei Cai; Jundong Li; Jie Wang 005; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Graph U-Nets. - Hongyang Gao; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Non-Local Graph Neural Networks. - Meng Liu 015; Zhengyang Wang; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Interpreting Image Classifiers by Generating Discrete Masks. - Hao Yuan 001; Lei Cai; Xia Hu 001; Jie Wang 005; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Augmented Equivariant Attention Networks for Microscopy Image Transformation. - Yaochen Xie; Yu Ding 002; Shuiwang Ji (IEEE Trans. Medical Imaging, 2022)

Learning Fair Graph Representations via Automated Data Augmentations. - Hongyi Ling; Zhimeng Jiang; Youzhi Luo; Shuiwang Ji; Na Zou 001 (ICLR, 2023)

Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models. - Meng Liu 015; Haoran Liu; Shuiwang Ji (ICLR, 2023)

Automated Data Augmentations for Graph Classification. - Youzhi Luo; Michael McThrow; Wing Yee Au 002; Tao Komikado; Kanji Uchino; Koji Maruhashi; Shuiwang Ji (ICLR, 2023)

Learning Hierarchical Protein Representations via Complete 3D Graph Networks. - Limei Wang; Haoran Liu; Yi Liu 059; Jerry Kurtin; Shuiwang Ji (ICLR, 2023)

Group Equivariant Fourier Neural Operators for Partial Differential Equations. - Jacob Helwig; Xuan Zhang; Cong Fu 003; Jerry Kurtin; Stephan Wojtowytsch; Shuiwang Ji (ICML, 2023)

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction. - Yuchao Lin; Keqiang Yan; Youzhi Luo; Yi Liu 059; Xiaoning Qian; Shuiwang Ji (ICML, 2023)

Graph Mixup with Soft Alignments. - Hongyi Ling; Zhimeng Jiang; Meng Liu 015; Shuiwang Ji; Na Zou 001 (ICML, 2023)

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian. - Haiyang Yu 005; Zhao Xu 005; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (ICML, 2023)

Graph and Geometry Generative Modeling for Drug Discovery. - Minkai Xu; Meng Liu 015; Wengong Jin; Shuiwang Ji; Jure Leskovec; Stefano Ermon (KDD, 2023)

Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. - Cong Fu 003; Jacob Helwig; Shuiwang Ji (LoG, 2023)

A Latent Diffusion Model for Protein Structure Generation. - Cong Fu 003; Keqiang Yan; Limei Wang; Wing Yee Au 002; Michael McThrow; Tao Komikado; Koji Maruhashi; Kanji Uchino; Xiaoning Qian; Shuiwang Ji (LoG, 2023)

A new perspective on building efficient and expressive 3D equivariant graph neural networks. - Weitao Du; Yuanqi Du; Limei Wang; Dieqiao Feng; Guifeng Wang; Shuiwang Ji; Carla P. Gomes; Zhi-Ming Ma (NeurIPS, 2023)

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization. - Shurui Gui; Meng Liu 015; Xiner Li; Youzhi Luo; Shuiwang Ji (NeurIPS, 2023)

Video Timeline Modeling For News Story Understanding. - Meng Liu 015; Mingda Zhang; Jialu Liu; Hanjun Dai; Ming-Hsuan Yang 001; Shuiwang Ji; Zheyun Feng; Boqing Gong (NeurIPS, 2023)

Towards Symmetry-Aware Generation of Periodic Materials. - Youzhi Luo; Chengkai Liu; Shuiwang Ji (NeurIPS, 2023)

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules. - Haiyang Yu 005; Meng Liu 015; Youzhi Luo; Alex Strasser; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (NeurIPS, 2023)

Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution. - Jie Wang 005; Rui Yang 031; Zijie Geng; Zhihao Shi; Mingxuan Ye; Qi Zhou 008; Shuiwang Ji; Bin Li 025; Yongdong Zhang 001; Feng Wu 001 (CoRR, 2023)

Provably Convergent Subgraph-wise Sampling for Fast GNN Training. - Jie Wang 005; Zhihao Shi; Xize Liang; Shuiwang Ji; Bin Li 025; Feng Wu 001 (CoRR, 2023)

A Score-Based Model for Learning Neural Wavefunctions. - Xuan Zhang; Shenglong Xu; Shuiwang Ji (CoRR, 2023)

Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization. - Xiner Li; Shurui Gui; Youzhi Luo; Shuiwang Ji (CoRR, 2023)

Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings. - Jie Wang 005; Zhanqiu Zhang; Zhihao Shi; Jianyu Cai; Shuiwang Ji; Feng Wu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Self-Supervised Learning of Graph Neural Networks: A Unified Review. - Yaochen Xie; Zhao Xu 005; Jingtun Zhang; Zhengyang Wang; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping. - Xinyi Xu; Zhengyang Wang; Cheng Deng; Hao Yuan 001; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Group Contrastive Self-Supervised Learning on Graphs. - Xinyi Xu; Cheng Deng; Yaochen Xie; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Explainability in Graph Neural Networks: A Taxonomic Survey. - Hao Yuan 001; Haiyang Yu 005; Shurui Gui; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Deep Low-Shot Learning for Biological Image Classification and Visualization From Limited Training Samples. - Lei Cai; Zhengyang Wang; Rob Kulathinal; Sudhir Kumar 001; Shuiwang Ji (IEEE Trans. Neural Networks Learn. Syst., 2023)

Second-Order Pooling for Graph Neural Networks. - Zhengyang Wang; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery. - Yu Zhang 044; Xiusi Chen; Bowen Jin; Sheng Wang 012; Shuiwang Ji; Wei Wang 010; Jiawei Han 001 (EMNLP, 2024)

On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods. - Montgomery Bohde; Meng Liu 015; Alexandra Saxton; Shuiwang Ji (ICLR, 2024)

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm. - Shurui Gui; Xiner Li; Shuiwang Ji (ICLR, 2024)

Complete and Efficient Graph Transformers for Crystal Material Property Prediction. - Keqiang Yan; Cong Fu 003; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (ICLR, 2024)

SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations. - Xuan Zhang; Jacob Helwig; Yuchao Lin; Yaochen Xie; Cong Fu 003; Stephan Wojtowytsch; Shuiwang Ji (ICLR, 2024)

Position: TrustLLM: Trustworthiness in Large Language Models. - Yue Huang; Lichao Sun 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Yuan Li; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang; Xiner Li; Hanchi Sun; et al. (ICML, 2024)

Graph Structure Extrapolation for Out-of-Distribution Generalization. - Xiner Li; Shurui Gui; Youzhi Luo; Shuiwang Ji (ICML, 2024)

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency. - Yuchao Lin; Jacob Helwig; Shurui Gui; Shuiwang Ji (ICML, 2024)

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction. - Keqiang Yan; Alexandra Saxton; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (ICML, 2024)

Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation. - Keqiang Yan; Xiner Li; Hongyi Ling; Kenna Ashen; Carl Edwards; Raymundo Arróyave; Marinka Zitnik; Heng Ji 001; Xiaofeng Qian; Xiaoning Qian; Shuiwang Ji (NeurIPS, 2024)

Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems. - Cong Fu 003; Xuan Zhang; Huixin Zhang; Hongyi Ling; Shenglong Xu; Shuiwang Ji (SDM, 2024)

3D Molecular Geometry Analysis with 2D Graphs. - Zhao Xu 005; Yaochen Xie; Youzhi Luo; Xuan Zhang; Xinyi Xu; Meng Liu 015; Kaleb Dickerson; Cheng Deng; Maho Nakata; Shuiwang Ji (SDM, 2024)

TrustLLM: Trustworthiness in Large Language Models. - Lichao Sun 001; Yue Huang 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang 001; Xiner Li; Zhengliang Liu; Yixin Liu 002; et al. (CoRR, 2024)

Geometry Informed Tokenization of Molecules for Language Model Generation. - Xiner Li; Limei Wang; Youzhi Luo; Carl Edwards; Shurui Gui; Yuchao Lin; Heng Ji 001; Shuiwang Ji (CoRR, 2024)

A Hierarchical Language Model For Interpretable Graph Reasoning. - Sambhav Khurana; Xiner Li; Shurui Gui; Shuiwang Ji (CoRR, 2024)

Political-LLM: Large Language Models in Political Science. - Lincan Li; Jiaqi Li; Catherine Chen 003; Fred Gui; Hongjia Yang; Chenxiao Yu; Zhengguang Wang; Jianing Cai; Junlong Aaron Zhou; Bolin Shen; Alex Qian; Weixin Chen; et al. (CoRR, 2024)

A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils. - Jacob Helwig; Xuan Zhang; Haiyang Yu 005; Shuiwang Ji (CoRR, 2024)

FlowX: Towards Explainable Graph Neural Networks via Message Flows. - Shurui Gui; Hao Yuan 001; Jie Wang 005; Qicheng Lao; Kang Li 004; Shuiwang Ji (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction. - Zhao Xu 005; Haiyang Yu 005; Montgomery Bohde; Shuiwang Ji (Trans. Mach. Learn. Res., 2024)

Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm. - Meng Liu 015; Haiyang Yu 005; Shuiwang Ji (Trans. Mach. Learn. Res., 2024)

Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies. - Yaochen Xie; Ziqian Xie; Sheikh Muhammad Saiful Islam; Degui Zhi; Shuiwang Ji (Trans. Mach. Learn. Res., 2024)

Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning. - Haoyu Han 001; Yaochen Xie; Hui Liu 003; Xianfeng Tang; Sreyashi Nag; William Headden; Yang Li 055; Chen Luo 003; Shuiwang Ji; Qi He 002; Jiliang Tang (ACL (Findings), 2025)

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association. - Weiqi Wang; Limeng Cui; Xin Liu; Sreyashi Nag; Wenju Xu; Chen Luo 003; Sheikh Muhammad Sarwar; Yang Li; Hansu Gu; Hui Liu; Changlong Yu; Jiaxin Bai; et al. (ACL (1), 2025)

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models. - Cong Fu 003; Xiner Li; Blake Olson; Heng Ji 001; Shuiwang Ji (ICLR, 2025)

Learning to Discover Regulatory Elements for Gene Expression Prediction. - Xingyu Su; Haiyang Yu 005; Degui Zhi; Shuiwang Ji (ICLR, 2025)

Eliminating Position Bias of Language Models: A Mechanistic Approach. - Ziqi Wang 003; Hanlin Zhang 002; Xiner Li; Kuan-Hao Huang; Chi Han; Shuiwang Ji; Sham M. Kakade; Hao Peng 009; Heng Ji 001 (ICLR, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

Hierarchical Language Model Design For Interpretable Graph Reasoning. - Sambhav Khurana; Xiner Li; Shurui Gui; Shuiwang Ji (Trans. Mach. Learn. Res., 2025)

Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability. - Hongyi Ling; Zhimeng Jiang; Na Zou 001; Shuiwang Ji (Trans. Mach. Learn. Res., 2025)

Discovering Physics Laws of Dynamical Systems via Invariant Function Learning. - Shurui Gui; Xiner Li; Shuiwang Ji (CoRR, 2025)

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra. - Montgomery Bohde; Mrunali Manjrekar; Runzhong Wang; Shuiwang Ji; Connor W. Coley (CoRR, 2025)

Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights. - Shubham Parashar; Blake Olson; Sambhav Khurana; Eric Li; Hongyi Ling; James Caverlee; Shuiwang Ji (CoRR, 2025)

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design. - Masatoshi Uehara; Xingyu Su; Yulai Zhao 002; Xiner Li; Aviv Regev; Shuiwang Ji; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Complex LLM Planning via Automated Heuristics Discovery. - Hongyi Ling; Shubham Parashar; Sambhav Khurana; Blake Olson; Anwesha Basu; Gaurangi Sinha; Zhengzhong Tu; James Caverlee; Shuiwang Ji (CoRR, 2025)

Dynamic Search for Inference-Time Alignment in Diffusion Models. - Xiner Li; Masatoshi Uehara; Xingyu Su; Gabriele Scalia; Tommaso Biancalani; Aviv Regev; Sergey Levine; Shuiwang Ji (CoRR, 2025)

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures. - Keqiang Yan; Montgomery Bohde; Andrii Kryvenko; Ziyu Xiang; Kaiji Zhao; Siya Zhu; Saagar Kolachina; Doguhan Saritürk; Jianwen Xie; Raymundo Arróyave; Xiaoning Qian; Xiaofeng Qian; et al. (CoRR, 2025)

Generative AI for Autonomous Driving: Frontiers and Opportunities. - Yuping Wang; Shuo Xing; Cui Can; Renjie Li 003; Hongyuan Hua; Kexin Tian; Zhaobin Mo; Xiangbo Gao; Keshu Wu; Sulong Zhou; Hengxu You; Juntong Peng; et al. (CoRR, 2025)

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association. - Weiqi Wang 001; Limeng Cui; Xin Liu; Sreyashi Nag; Wenju Xu; Chen Luo 003; Sheikh Muhammad Sarwar; Yang Li; Hansu Gu; Hui Liu; Changlong Yu; Jiaxin Bai; et al. (CoRR, 2025)

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists. - Lianhao Zhou; Hongyi Ling; Keqiang Yan; Kaiji Zhao; Xiaoning Qian; Raymundo Arróyave; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning. - Shubham Parashar; Shurui Gui; Xiner Li; Hongyi Ling; Sushil Vemuri; Blake Olson; Eric Li; Yu Zhang 044; James Caverlee; Dileep Kalathil; Shuiwang Ji (CoRR, 2025)

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling. - Jacob Helwig; Sai Sreeharsha Adavi; Xuan Zhang; Yuchao Lin; Felix S. Chim; Luke Takeshi Vizzini; Haiyang Yu 005; Muhammad Hasnain; Saykat Kumar Biswas; John J. Holloway; Narendra Singh; N. K. Anand; et al. (CoRR, 2025)

NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis. - Mouadh Yagoubi; David Danan; Milad Leyli-Abadi; Jocelyn Ahmed Mazari; Jean-Patrick Brunet; Abbas Kabalan; Fabien Casenave; Yuxin Ma; Giovanni Catalani; Jean Fesquet; Jacob Helwig; Xuan Zhang; et al. (CoRR, 2025)

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames. - Haiyang Yu 005; Yuchao Lin; Xuan Zhang; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training. - Shurui Gui; Shuiwang Ji (CoRR, 2025)

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials. - Cong Fu 003; Yuchao Lin; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design. - Xingyu Su; Xiner Li; Masatoshi Uehara; Sunwoo Kim; Yulai Zhao 002; Gabriele Scalia; Ehsan Hajiramezanali; Tommaso Biancalani; Degui Zhi; Shuiwang Ji (CoRR, 2025)

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations. - Yuchao Lin; Cong Fu 003; Zachary Krueger; Haiyang Yu 005; Maho Nakata; Jianwen Xie; Emine Küçükbenli; Xiaofeng Qian; Shuiwang Ji (CoRR, 2025)

Language Models for Controllable DNA Sequence Design. - Xingyu Su; Xiner Li; Yuchao Lin; Ziqian Xie; Degui Zhi; Shuiwang Ji (CoRR, 2025)