Random Regression for Bayes Nets Applied to Relational Data. - Oliver Schulte; Hassan Khosravi; Tianxiang Gao; Yuke Zhu (StarAI@UAI, 2012)

Learning compact Markov logic networks with decision trees. - Hassan Khosravi; Oliver Schulte; Jianfeng Hu; Tianxiang Gao (Mach. Learn., 2012)

Modelling relational statistics with Bayes Nets. - Oliver Schulte; Hassan Khosravi; Arthur E. Kirkpatrick; Tianxiang Gao; Yuke Zhu (Mach. Learn., 2014)

Drug-induced mRNA Signatures Are Enriched for the Minority of Genes that Are Highly Heritable. - Tianxiang Gao; Petter Brodin; Mark M. Davis; Vladimir Jojic (Pacific Symposium on Biocomputing, 2015)

Minimum-volume-regularized weighted symmetric nonnegative matrix factorization for clustering. - Tianxiang Gao; Sigurdur Olofsson; Songtao Lu (GlobalSIP, 2016)

Degrees of Freedom in Deep Neural Networks. - Tianxiang Gao; Vladimir Jojic (UAI, 2016)

Extracting information from deep learning models for computational biology. - Tianxiang Gao (2017)

DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization. - Tianxiang Gao; Chris Chu (AAAI, 2018)

DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity. - Tianxiang Gao; Weiming Bao; Jinning Li 001; Xiaofeng Gao 001; Boyuan Kong; Yan Tang; Guihai Chen; Xuan Li (DEXA (1), 2018)

Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems. - Tianxiang Gao; Songtao Lu; Jia Liu 002; Chris Chu (CoRR, 2019)

A Forest from the Trees: Generation through Neighborhoods. - Yang Li 012; Tianxiang Gao; Junier Oliva (AAAI, 2020)

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization. - Tianxiang Gao; Songtao Lu; Jia Liu 002; Chris Chu (CoRR, 2020)

On the Convergence of Randomized Bregman Coordinate Descent for Non-Lipschitz Composite Problems. - Tianxiang Gao; Songtao Lu; Jia Liu 002; Chris Chu (ICASSP, 2021)

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems. - Xiaocong Du; Bhargav Bhushanam; Jiecao Yu; Dhruv Choudhary; Tianxiang Gao; Sherman Wong; Louis Feng; Jongsoo Park; Yu Cao 001; Arun Kejariwal (ICMLA, 2021)

Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation. - Ruiwei Feng; Xiangshang Zheng; Tianxiang Gao; Jintai Chen; Wenzhe Wang; Danny Z. Chen; Jian Wu 001 (IEEE Trans. Medical Imaging, 2021)

A global convergence theory for deep ReLU implicit networks via over-parameterization. - Tianxiang Gao; Hailiang Liu; Jia Liu 002; Hridesh Rajan; Hongyang Gao (ICLR, 2022)

Gradient Descent Optimizes Infinite-Depth ReLU Implicit Networks with Linear Widths. - Tianxiang Gao; Hongyang Gao (CoRR, 2022)

On the optimization and generalization of overparameterized implicit neural networks. - Tianxiang Gao; Hongyang Gao (CoRR, 2022)

Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models. - Tianxiang Gao; Xiaokai Huo; Hailiang Liu; Hongyang Gao (NeurIPS, 2023)

HC-MVSNet: A probability sampling-based multi-view-stereo network with hybrid cascade structure for 3D reconstruction. - Tianxiang Gao; Zijian Hong; Yixing Tan; Lizhuo Sun; Yichen Wei; Jianwei Ma (Pattern Recognit. Lett., 2024)

Global Convergence in Neural ODEs: Impact of Activation Functions. - Tianxiang Gao; Siyuan Sun; Hailiang Liu; Hongyang Gao (ICLR, 2025)

Visualizing the Local Atomic Environment Features of Machine Learning Interatomic Potential. - Xuqiang Shao; Yuqi Zhang; Di Zhang; Tianxiang Gao; Xinyuan Liu; Zhiran Gan; Fanshun Meng; Hao Li; Weijie Yang (CoRR, 2025)