PUBLICATION

Latest Preprints:

[AI4Bio]
Le Song, Eran Segal, and E. P. Xing, Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating, and Programming Biology at All Levels, A position paper, arXiv:2412.06993, 2024.

Caleb N. Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, E. P. Xing, Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale, NeurIPS AIDrugX Workshop, 2024. (bioRXiv:10.1101/2024.12.01.625444)

Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, E. P. Xing, A Large-Scale Foundation Model for RNA Function and Structure Prediction, NeurIPS AIDrugX Workshop, (Spotlight), 2024. (bioRXiv:10.1101/2024.11.28.625345)

Ning Sun, Shuxian Zou, Tianhua Tao, Sazan Mahbub, Xavier Cheng, Yonghao Zhuang, Hongyi Wang, Le Song, E. P. Xing, Mixture of Experts Enable Efficient and Effective Protein Understanding and Design., NeurIPS AIDrugX Workshop, (Spotlight), 2024. (bioRXiv:10.1101/2024.11.29.625425)

Nicholas Ho, Caleb N. Ellington, Jinyu Hou, Sohan Addagudi, Shentong Mo, Tianhua Tao, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, E. P. Xing, Scaling Dense Representations for Single Cell with Transcriptome-Scale Context, NeurIPS AIDrugX Workshop, 2024. (bioRXiv:10.1101/2024.11.28.625303)

Jiayou Zhang, Barthelemey Meynard-Piganeau, James Gong, Xingyi Cheng, Yingtao Luo, Hugo Ly, Le Song, E. P. Xing, Balancing Locality and Reconstruction in Protein Structure Tokenizer, NeurIPS Machine Learning in Structural Biology Workshop, 2024. (bioRXiv:10.1101/2024.12.02.626366)

Peter Lee, Xingyi Chen, Le Song, E. P. Xing, Retrieval Augmented Protein Language Models for Protein Structure Prediction, NeurIPS Machine Learning in Structural Biology Workshop, 2024. (bioRXiv:10.1101/2024.12.02.626519)

Andrew T. McNutt, Abhinav K. Adduri, Caleb N. Ellington, Monica T. Dayao, E. P. Xing, Hosein Mohimani, David R. Koes,SPRINT Enables Interpretable and Ultra-Fast Virtual Screening against Thousands of Proteomes, NeurIPS Machine Learning in Structural Biology Workshop, 2024.

Ethan Wu, Caleb Ellington, Ben Lengerich, E. P. Xing, Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis, Machine Learning for Health Symposium, 2024.

Sazan Mahbub, Caleb Ellington, Sina Alinejad, Kevin Wen, Yingtao Luo, Ben Lengerich, E. P. Xing, From One to Zero: RAG-IM Adapts Language Models for Interpretable Zero-Shot Clinical Predictions, Machine Learning for Health Symposium, 2024.

Caleb Ellington, Benjamin Lengerich, Thomas B.K. Watkins, Jiekun Yang, Abhinav K Adduri, Sazan Mahbub, Hanxi Xiao, Manolis Kellis, E. P. Xing Learning to estimate sample-specific transcriptional networks for 7000 tumor, bioRxiv, 2023.12.01.569658, under review at PNAS, 2024.

Ding Bai, Shentong Mo, Ruiyi Zhang, Yingtao Luo, Jiahao Gao, Jeremy Yang, Qiuyang Wu, Digvijay Singh, Hamidreza Rahmani, Tiffany Amariuta, Danielle Grotjahn, Sheng Zhong, Nathan Lewis, Wei Wang, Trey Ideker, Pengtao Xie, E. P. Xing, scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics, bioRxiv, 2024.11. 09.622759, under review at Nature Methods, 2024.

Li Zhang, Han Guo, Leah Schaffer, Young Su Ko, Digvijay Singh, Hamid Rahmani, Danielle Grotjahn, Michael Gilson, Wei Wang, Trey Ideker, E. P. Xing, Pengtao Xie, ProteinAligner: A Multi-modal Pretraining Framework for Protein Foundation Models, bioRxiv, 2024.10. 06.616870, In review at Nature Communications, 2024.

Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J. Sofroniew, Fabian Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake, How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities, to appear, Cell, 2024.

[Artificial General Intelligence]


Z. Hu and E. P. Xing, World Model, In preparation, 2024.

Jiannan Xiang, Guangyi Liu, Yi Gu, Qiyue Gao, Yuting Ning, Yuheng Zha, Zeyu Feng, Tianhua Tao, Shibo Hao, Yemin Shi, Zhengzhong Liu, E. P. Xing and Zhiting Hu, Pan: Towards General World Model with Natural Language Actions and Video States, arXiv:2406.09455, 2024.

Z. Hu and E. P. Xing, Panoramic Learning with A Standardized Machine Learning Formalism, arXiv 2108.07783

[Large Language Models]


Hector Liu and E. P. Xing, K2: Build A 65B LLM From Scratch, In preparation, 2024.

N Sengupta, et al, and E. P. Xing, Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models, arXiv preprint arXiv:2308.16149, 2023.

Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, Ion Stoica, E. P. Xing, Vicuna: An open-source chat-bot impressing gpt-4 with 90% chatgpt quality, Blog, 2023.

Selected Publications:



Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Timothy Baldwin, E. P. Xing, LLM360: Towards Fully Transparent Open-Source LLMs, Proceedings of the 1st Conference on Language Modeling, 2024. (CoLM '24)

Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, E. P. Xing, Hao Zhang, Joseph E Gonzalez, Ion Stoica, Judging LLM-as-a-judge with MT-bench and Chatbot Arena, Advances in Neural Information Processing Systems 37, MIT Press, 2023. (NeurIPS 23) (NeurIPS 23)

Z. Hu and E. P. Xing, Towards A "Standard Model" of Machine Learning, Harvard Data Science Review, 2022.

L. Zheng, Z. Li, H Zhang, Y. Zhuang, Z. Chen, Y. Huang, Y. Wang, Y. Xu, D Zhuo, E. P. Xing, J. E. Gonzalez, and I. Stoica, Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning, The 16th USENIX Symposium on Operating Systems Design and Implementation, 2022. (OSDI 22)

A. Qiao, S. K. Choe, S. J. Subramanya, W. Neiswanger, Q. Ho, H. Zhang, G. R. Ganger, and E. P. Xing, Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning, The 15th USENIX Symposium on Operating Systems Design and Implementation, 2021 (OSDI '21). (Recipient of the Jay Lepreau Best Paper Award)

H. Wang, S. Ge, Z. Lipton, and E. P. Xing, Learning Robust Global Representations by Penalizing Local Predictive Power, Advances in Neural Information Processing Systems, 2019 (NeurIPS 2019).

B. Aragam, C. Dan, P. Ravikumar, and E. P. Xing, Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering, Annals of Statistics, 2019

H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, M. Jordan, Theoretically Principled Trade-off between Robustness and Accuracy, Proceedings of the 36th International Conference on Machine Learning, 2019 (ICML 2019).

K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing, Neural Architecture Search with Bayesian Optimisation and Optimal Transport, Advances in Neural Information Processing Systems 32. (NeurIPS '18) (Recipient of the Nvidia Pioneering Research Award.) ( arXiv:1802.07191, communicated 11 Feb 2018.)

X. Zheng, B. Aragam, P. Ravikumar, and E. P. Xing, DAGs with NO TEARS: Continuous Optimization for Structure Learning, Advances in Neural Information Processing Systems 32. (NeurIPS 18)

Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, Controllable Text Generation, The 34th International Conference on Machine Learning. (ICML 2017).

Z. Hu, X. Ma, Z. Liu, E. Hovy and E.P. Xing, Harnessing Deep Neural Networks with Logic Rules, 54th Annual Meeting of the Association for Computational Linguistics. (ACL 2016). (Recipient of the OUTSTANDING PAPER Award) ( arXiv:1603.06318, communicated 21 Mar 2016.)

E. P. Xing, Q. Ho, P. Xie, W. Dai, Strategies and Principles of Distributed Machine Learning on Big Data, Engineering, Volume:2, pp179 - 95, 2016.

J. Zhu, N. Chen, and E. P. Xing, Bayesian Inference with Posterior Regularization, and applications to Infinite Latent SVMs , Journal of Machine Learning Research, 15:1799-1847, 2014.

W. Neiswanger, C. Wang and E. P. Xing, Asymptotically Exact, Embarrassingly Parallel MCMC, Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2014). ( arXiv:1311.4780, communicated 19 Nov 2013.)

Q. Ho, J. Cipar, H. Cui, J.-K. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger and E. P. Xing, More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Response Regression with Structured Sparsity, with applications to eQTL Mapping, Annals of Applied Statistics, Volume 6, Number 3 (2012), 1095-1117. [pdf]

M. Kolar, L. Song, A. Ahmed, and E. P. Xing, Estimating Time-Varying Networks , Annals of Applied Statistics, Vol. 4, No. 1, 94 - 123, 2010.

S. Hanneke, W. Fu and E. P. Xing, Discrete Temporal Models of Social Networks, Electronic Journal of Statistics Vol. 4 (2010) 585-605. (arXiv:0908.1258, communicated August 2009.)

E. Airoldi, D. Blei, S. Fienberg, and E. P. Xing, Mixed Membership Stochastic Blockmodel, Journal of Machine Learning Research, 9(Sep):1981--2014, 2008.

E. P. Xing, A.Y. Ng, M.I. Jordan and S. Russell, Distance Metric Learning, with application to Clustering with side-information, Advances in Neural Information Processing Systems 16 (NIPS2002), (eds. Becker et al.) MIT Press, 521-528, 2002.( data, code.)

H. Zhang, Z. Deng, X. Liang, L. Yang, S. Xu, J. Zhu, and E. P. Xing, Structured Generative Adversarial Networks, Proceedings of Advances in Neural Information Processing Systems 31 (NIPS '17). (Recipient of the Nvidia Pioneering Research Award)

P. Xie and E. P. Xing Deep Conditional Determinantal Point Process for Large-Scale Multi-Label Classification on Youtube-8M and Open Images Datasets, Proceedings of 19th International Conference on Computer Vision (ICCV '17)

P. Goyal, Z. Hu, X.~Liang, C. Wang, and E. P. Xing Nonparametric Variational Auto-encoders for Hierarchical Representation Learning, Proceedings of 19th International Conference on Computer Vision (ICCV '17)

X. Liang, Z. L. Lee, W. Dai, and E. P. Xing Dual Motion GAN for Future-Flow Embedded Video Prediction, Proceedings of 19th International Conference on Computer Vision (ICCV '17)

X. Liang, Z. Hu, H. Zhang, C. Gan, and E. P. Xing Recurrent Topic-Transition GAN for Visual Paragraph Generation, Proceedings of 19th International Conference on Computer Vision (ICCV '17)

M. Sachan, K. Dubey, and E. P. Xing From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems, Proceeding of the 2017 Conference on Empirical Methods on Natural Language Processing. (EMNLP '17)

P. Xie, B. Poczos, and E. P. Xing Near-Orthogonality Regularization in Kernel Methods, Proceedings of the 33rd International Conference on Conference on Uncertainty in Artificial Intelligence. (UAI '17)

I. Yen, X. Huang, W. Dai, P. Ravikumar, I. Dhillon, and E. P. Xing, A Parallel and Primal-Dual Sparse Method for Extreme Classification, Proceedings of The 23rd ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2017).

J. He, Z. Hu, T. Berg-Kirkpatrick, Y. Huang, and E. P. Xing, Efficient Correlated Topic Modeling with Topic Embedding, Proceedings of The 23rd ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2017).

K. Zhang, C. Liu, J. Zhang, H. Xiong, E. P. Xing, and J. Ye, Finding Structures of Large Matrices Through Compression, Proceedings of The 23rd ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2017).

W. Neiswanger and E. P. Xing, Post-Inference Prior Swapping, The 34th International Conference on Machine Learning. (ICML 2017).

P. Xie and E. P. Xing, Cosine Similarity Constrained Latent Space Models, The 34th International Conference on Machine Learning. (ICML 2017).

P. Xie, A. Singh and E. P. Xing, Uncorrelation and Evenness: A New Diversity-Promoting Regularizer, The 34th International Conference on Machine Learning. (ICML 2017).

Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov and E. P. Xing, Controllable Text Generation, The 34th International Conference on Machine Learning. (ICML 2017).

H. Zhang, Z. Zeng, W. Dai, Q. Ho and E. P. Xing, Poseidon: An Efficient Communication Interface for Distributed Deep Learning on GPU Clusters., USENIX Annual Technical Conference (ATC 2017).

P. Xie and E.P. Xing, A Constituent-Centric Neural Architecture for Reading Comprehension, 55th Annual Meeting of the Association for Computational Linguistics. (ACL 2017).

L. Qin, Z. Zhang, H. Zhao, Zhiting Hu and E.P. Xing, Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification , 55th Annual Meeting of the Association for Computational Linguistics. (ACL 2017).

X. Liang, L. Lin, X. Shen, J. Feng, S. Yan and E. P. Xing, Interpretable Structure-Evolving LSTM, Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).

X. Liang, L. Lee, and E. P. Xing, Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection, Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).

M. Law, Y. Yu, R. Urtasun, R. S. Zemel and E. P. Xing, Efficient Multiple Instance Metric Learning using Weakly Supervised Data, Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).

M. Xu, X. Chai, H. Muthakana, X. Liang, G. Yang, T. Zeev-Ben-Mordehai and E. P. Xing, Deep learning based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms, the Twenty-fifth International Conference on Intelligence Systems for Molecular Biology (ISMB 2017). Bioinformatics (2017) in press. [pdf]

Y. Zhou, K. Yuan, Y. Yu, X. Ni, P. Xie, E. P. Xing and S. Xu, Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions, Heredity (Edinb), 118(5):503-510, 2017.

M. Al-Shedivat, A. G. Wilson, Y. Saatchi, Z. Hu, and E. P. Xing, Learning Scalable Deep Kernels with Recurrent Structure, Journal of Machine Learning Research, in press, 2017. ( arXiv:1610.08936, communicated 27 Oct 2016.)

2016

A. Dubey, S. J. Reddi, S. Williamson, B. Poczos, A. J. Smola and E. P. Xing, Variance Reduction in Stochastic Gradient Langevian Dynamics, Advances in Neural Information Processing Systems 30 (NIPS 2016).

M. Al-Shedivat, K. Kandasamy and E. P. Xing, Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices, Advances in Neural Information Processing Systems 30 (NIPS 2016).

Z. Hu, A. Wilson, R. Salakhutdinov and E. P. Xing, Stochastic Variational Deep Kernel Learning, Advances in Neural Information Processing Systems 30 (NIPS 2016).

E. P. Xing, Q. Ho, P. Xie, W. Dai, Strategies and Principles of Distributed Machine Learning on Big Data, Engineering, Volume:2, pp179 - 95, 2016.

Z. Hu, Z. Yang, R. Salakhutdinov and E. P. Xing, Deep Neural Networks with Massive Learned Knowledge, Proceeding of the 2016 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2016).

M. Sachan and E.P. Xing, Easy Questions First? Curriculum Learning for Question Answering , 54th Annual Meeting of the Association for Computational Linguistics. (ACL 2016).

Z. Hu, X. Ma, Z. Liu, E. Hovy and E.P. Xing, Harnessing Deep Neural Networks with Logic Rules, 54th Annual Meeting of the Association for Computational Linguistics. (ACL 2016). (Recipient of the OUTSTANDING PAPER Award) ( arXiv:1603.06318, communicated 21 Mar 2016.)

H. Zhang, Z. Hu, Y. Deng, M. Sachan, Z. Yan and E.P. Xing, Learning Concept Taxonomies from Multi-modal Data, 54th Annual Meeting of the Association for Computational Linguistics. (ACL 2016).

M. Sachan and E.P. Xing, Machine Comprehension using Rich Semantic Representations, 54th Annual Meeting of the Association for Computational Linguistics. (ACL 2016).

M. Sachan and E.P. Xing, Science Question Answering using Instructional Materials, 54th Annual Meeting of the Association for Computational Linguistics. (ACL 2016).

P. Xie, J. Kim, Y. Zhou, Q. Ho, A. Kumar, Y. Yu and E. P. Xing, Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting, Proceedings of the 32nd International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2016). ( arXiv:1311.4780, communicated 19 Nov 2013.)

Y. Tan, Z. Fan, G. Li, F. Wang, Z. Li, S. Liu, Q. Pan, Q. Ho and E. P. Xing, Scalable Time-Decaying Adaptive Prediction Algorithm, Proceedings of The 22nd ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2016).

S. Lee, S. Kong and E. P. Xing, A Network-driven Approach for Genome-wide Association Mapping, the Twenty-fourth International Conference on Intelligence Systems for Molecular Biology (ISMB 2016). Bioinformatics (2016) in press. [pdf]

Y. Wang, W. Neiswanger, W. Dai, V. Sadhanala, S. Sra and E. P. Xing, Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms, The 33rd International Conference on Machine Learning. (ICML 2016).

P. Xie, J. Zhu and E. P. Xing, Diversity-Promoting Bayesian Learning of Latent Variable Models, The 33rd International Conference on Machine Learning. (ICML 2016).

Z. Hu, G. Luo, M. Sachan, Z. Nie and E.P. Xing, Grounding Topic Models with Knowledge Bases, The 25th International Joint Conference on Artificial Intelligence. (IJCAI 2016).

J. Kim, Q. Ho, S. Lee, X. Zheng, W. Dai, G. Gibson and E.P. Xing, STRADS: A Distributed Frame- work for Scheduled Model Parallel Machine Learning, European Conference on Computer Systems, 2016 (EuroSys 2016).

H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons and E.P. Xing, GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server, European Conference on Computer Systems, 2016 (EuroSys 2016).

M. Law, Y. Yu, M. Gord and E. P. Xing, Closed-Form Training of Mahalanobis Distance for Supervised Clustering, Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016).

X. Chang, Y. Yu, Y. Yang and E. P. Xing, They Are Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers., Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016).

A. Wilson, Z. Hu, R. Salakhudinov and E. P. Xing Deep Kernel Learning, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (AISTATS 2016). [Supplemental Material]

Y. Zhou, Y. Yu, Y. Liang and E. P. Xing On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (AISTATS 2016).

H. Cheng, Y. Yu, X.Zhang, E. P. Xing, and D. Schurmans Scalable and Sound Low-Rank Tensor Learning, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (AISTATS 2016).

A. Wilson, W. Herlands, S. Flaxman, H. Nickisch, W. Van Panhuis, D. Neill and E. P. Xing Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (AISTATS 2016). [Supplemental Material]

A. Dubey, J. Oliva, A. Wilson, E. P. Xing, B. Poczos, and J. Schneider, Bayesian Nonparametric Kernel-Learning, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. (AISTATS 2016).

M. Marchetti-Bowick, J. Yin, J. Howrylak and E. P. Xing, A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits, Bioinformatics, 32 (19):btw347, 2016. [pdf]

L. Song, H. Liu, A. Parikh, and E. P. Xing, Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning, Journal of Machine Learning Research, 2016. ( arXiv:1401.3940, communicated 16 Jan 2014.)

Q. Ho, J. Yin and E. P. Xing, Latent Space Inference of Internet-Scale Networks, Journal of Machine Learning Research, 17(78):1 - 41, 2016.

J. Howrylak, M. Moll, B. Raby, S. Weiss, W. Wu, and E. P. Xing, Gene Expression Profiling of Asthma Phenotypes Demonstrates Molecular Signatures of Atopy and Asthma Control, Journal of Allergy and Clinical Immunology, Volume 137, Issue 5, Pages 1390- 1397, 2016.

S. Lee, A. Lozano, P. Kambadur, and E. P. Xing, An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations, Journal of Computational Biology, 23(5):372 - 89, 2016.

Z. Guo, Z. Zhang, E. P. Xing, and C. Faloutsos, Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning, ACM Transactions on Knowledge Discovery from Data, Volume 10 Issue 3, February 2016.

2015

E. P. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y. Yu, Petuum: A new Platform for Distributed Machine Learning on Big Data, IEEE Transactions on Big Data, Volume:1 Issue:2, pp49 - 67, 2015. ( arXiv:1312.7651, communicated 30 Dec 2013.)

Bin Zhao and E. P. Xing, Sparse Output Coding for Scalable Visual Recognition, International Journal of Computer Vision, 119:60 - 75. [pdf]

W. Wang, Y. Liang, Lixin Shen, and E. P. Xing, Nonparametric Decentralized Detection and Sparse Sensor Selection via Weighted Kernel, IEEE Transactions on Signal Processing, Volume:64, Issue:2, pp306 - 321, 2015.

A. Martins, M. Figueiredo, P. Aguiar, N.A. Smith and E. P. Xing, AD3: Alternating Directions Dual Decomposition for MAP Inference in Graphical Models , Journal of Machine Learning Research, 16(Mar): 495-545, 2015. ( arXiv:1212.6550, communicated 28 Dec 2012.)

A. Wilson, C. Lucas, C. Dann and E. P. Xing, The Human Kernel, Advances in Neural Information Processing Systems 29 (eds. Daniel Lee and Masashi Sugiyama), MIT Press, 2015. (NIPS 2015).

J. Wei, W. Dai, A. Qiao, H. Cui, Q. Ho, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E.P. Xing, Managed Communication and Consistency for Fast Data-Parallel Iterative Analytics, ACM Symposium on Cloud Computing, 2015 (SoCC 2015). (Recipient of the Best Paper Award)

E. P. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y. Yu, Petuum: A new Platform for Distributed Machine Learning on Big Data, Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2015).

P. Xie, Y. Deng and E. P. Xing, Diversifying Restricted Boltzmann Machine for Document Modeling, Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2015).

X. Zheng, Y. Yu and E. P. Xing, Linear Time Samplers for Supervised Topic Models using Compositional Proposals, Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2015).

H. Zhang, G. Kim and E. P. Xing, Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data, Proceedings of The 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2015).

X. Chang, Y. Yang, A. G. Hauptmann, E. P. Xing and Y. Yu, Semantic Concept Discovery for Large- Scale Zero-Shot Event Detection, The 24th International Joint Conference on Artificial Intelligence. (IJCAI 2015).

M. Sachan, E. Hovy and E.P. Xing, An Active Learning approach to Coreference Resolution, The 24th International Joint Conference on Artificial Intelligence. (IJCAI 2015).

M. Sachan, A. Dubey, M.Richardson and E.P. Xing, Learning Answer-Entailing Structures for Machine Comprehension , 53rd Annual Meeting of the Association for Computational Linguistics. (ACL 2015). (Recipient of an Honorable Mentioning)

Z. Hu, P. Huang, Y. Deng, Y. Gao and E. P. Xing, Entity Hierarchy Embedding, 53rd Annual Meeting of the Association for Computational Linguistics. (ACL 2015).

X. Chang, Y. Yu and E. P. Xing, Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM, The 32th International Conference on Machine Learning. (ICML 2015).

Z. Hu, Q. Ho, A. Dubey and E. P. Xing, Large-scale Distributed Dependent Nonparametric Trees, The 32th International Conference on Machine Learning. (ICML 2015).

Z. Hu, J. Yao, B. Cui and E. P. Xing, Community Level Diffusion Extraction, Proceedings of the 2015 ACM SIGMOD international conference on Management of data. (SIGMOD 2015).

P. Xie, D. Yang and E. P. Xing, Incorporating Word Correlation Knowledge into Topic Modeling, Conference of the North American Chapter of the Association for Computational Linguistics. (NAACL 2015).

J. Yuan, F. Gao, Q. Ho, W. Dai, J. Wei, X. Zheng, E. P. Xing, T. Liu and W. Ma, LightLDA: Big Topic Models on Modest Compute Clusters, Proceedings of the 24th international conference on World Wide Web. (WWW 2015). ( arXiv:1412.1576, communicated 4 Dec 2014.)

Y. Yu, X. Zheng, M. Marchetti-Bowick and E. P. Xing, Minimizing Nonconvex Non-Separable Functions, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. (AISTATS 2015).

J. Oliva, W. Neiswanger, B. Poczos, E. P. Xing and J. Schneider, Fast Function to Function Regression, Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. (AISTATS 2015).

P. Xie and E. P. Xing, Integrating Image Clustering and Codebook Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. (AAAI 2015).

P. Xie, Y. Pei, Y. Xie, and E. P. Xing, Mining User Interests from Personal Photos, Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. (AAAI 2015).

W. Dai, A. Kumar, J. Wei. Q. Ho, G. Gibson and E. P. Xing, High-Performance Distributed ML at Scale through Parameter Server Consistency Models, Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. (AAAI 2015). ( arXiv:1312.7869, communicated 30 Dec 2013.) [Supplemental Material]

S. Lee, A. Lozano, P. Kambadur and E. P. Xing, An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations, 19th International Conference on Research in Computational Molecular Biology. (RECOMB 2015).

W. Wang, Y. Liang and E. P. Xing, Collective Support Recovery for Multi-Design Multi-Response Linear Regression, IEEE Transactions on Information Theory, VOL. 61, NO. 1, 2015.

2014

J. Eisenstein, B. O’Connor, N. A. Smith, and E. P. Xing, Diffusion of Lexical Change in Social Media, PLoS One, volume 9, Issue 11, e113114, 2014. ( arXiv:1210.5268, communicated 18 Oct 2012.) [pdf preprint]

S. Lee, J. K. Kim, X. Zheng, Q. Ho, G. A. Gibson, and E. P. Xing, On Model Parallelization and Scheduling Strategies for Distributed Machine Learning, Advances in Neural Information Processing Systems 28 (eds. Corinna Cortes and Neil Lawrence), MIT Press, 2014. (NIPS 2014). ( arXiv:1312.5766, communicated 19 Dec 2013.) [Supplemental Material]

A. Dubey, Q. Ho, S.Williamson, and E. P. Xing, Dependent Nonparametric Trees for Dynamic Hierarchical Clustering, Advances in Neural Information Processing Systems 28 (eds. Corinna Cortes and Neil Lawrence), MIT Press, 2014. (NIPS 2014). [Supplemental Material]

A. P. Parikh, A. Saluja, C. Dyer and E. P. Xing, Language Modeling with Power Low Rank Ensembles, Proceeding of the 2014 Conference on Empirical Methods on Natural Language Processing. (EMNLP 2014). (Recipient of the runner-up for BEST PAPER Award) ( arXiv:1312.7077, communicated 26 Dec 2013.) [Supplemental Material]

W. Neiswanger, C. Wang and E. P. Xing, Asymptotically Exact, Embarrassingly Parallel MCMC, Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2014). ( arXiv:1311.4780, communicated 19 Nov 2013.)

W. Neiswanger, C. Wang, Q. Ho and E. P. Xing, Modeling Citation Networks using Latent Random Offsets, Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2014).

A. Dubey, S.Williamson and E. P. Xing, Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models, Proceedings of the 30th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2014). [Supplemental Material]

H. Cui, A. Tumanov, J. Wei, L. Xu, W. Dai, J. Haber-Kucharsky, Q. Ho, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E.P. Xing, Exploiting Iterative-ness for Parallel ML Computations, ACM Symposium on Cloud Computing, 2014 (SoCC 2014).

H. Cui, J. Cipar, Q. Ho, J-K Kim, S. Lee, A. Kumar, J. Wei, W. Dai, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E. P. Xing, Exploiting Bounded Staleness to Speed up Big Data Analytics, USENIX Annual Technical Conference , June 19-20, 2014. Philadelphia, PA (ATC 2014).

A. Parikh, R. Curtis, I. Kuhn, S. Becker, M. Bissell, E. P. Xing and W. Wu, Network Analysis of Breast Cancer Progression and Reversal Using a Tree-evolving Network Algorithm, PLoS Computational Biology, Volume 10, Issue 7, e1003713, 2014. [pdf preprint].

E. P. Xing, R. Curtis, G. Schoenherr, S. Lee, J. Yin, K. Puniyani, W. Wu, P. Kinnaird, GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap, PLoS One, Volume 9, Issue 6, e97524, 2014. [pdf preprint].

S. Shringarpure and E. P. Xing, Effects of Sample Selection Bias on the Accuracy of Population Structure and Ancestry Inference, Genes, Genomes, Genetics, vol. 4 no. 5 901-911, 2014, [pdf preprint].

D. Yogatama, C. Wang, B.R. Routledge, N.A. Smith and E. P. Xing, Dynamic Language Models for Streaming Text, Transactions of the Association for Computational Linguistics 2:181-192, 2014 (TACL 2014).

A. Parikh, S. Cohen and E. P. Xing, Spectral Unsupervised Parsing with Additive Tree Metrics, The 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014).

G. Kim and E. P. Xing, Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos, Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014).

G. Kim, L. Sigal and E. P. Xing, Jointly Summarizing Large-Scale Web Images and Videos for the Storyline Reconstruction, Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014).

B. Zhao and E. P. Xing, Quasi Real-Time Summarization for Consumer Videos, Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014).

B. Zhao and E. P. Xing, Hierarchical Feature Hashing for Fast Dimensionality Reduction, Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014).

M. Kolar, H. Liu and E. P. Xing, Graph Estimation From Multi-attribute Data , Journal of Machine Learning Research, 15:1713-1750, 2014. ( arXiv:1210.7665, communicated 29 Oct 2012.)

J. Zhu, N. Chen, and E. P. Xing, Bayesian Inference with Posterior Regularization, and applications to Infinite Latent SVMs , Journal of Machine Learning Research, 15:1799-1847, 2014. ( arXiv:1210.1766)

A. Kumar, A. Beutel, Q. Ho and E. P. Xing, Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014).

J. B. Oliva, W. Neiswanger, B. Poczos, J. Schneider and E. P. Xing, Fast Distribution To Real Regression, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014).

W. Neiswanger, F. Wood and E. P. Xing, The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014).

A. Beutel, A. Kumar, E. E. Papalexakis, P. P. Talukdar, C. Faloutsos and E. P. Xing, FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop, Proceedings of The 14th SIAM International Conference on Data Mining (SDM 2014).

G. Kim and E. P. Xing, Visualizing Brand Associations from Web Community Photos, Proceedings of The 7th ACM International Conference on Web Search and Data Mining (WSDM 2014). [software and project page]

M. Sachan, A. Dubey, S. Srivastava, E. P. Xing and Eduard Hovy, Spatial Compactness meets Topical Consistency: Jointly modeling Links and Content for Community Detection , Proceedings of The 7th ACM International Conference on Web Search and Data Mining (WSDM 2014).

A. P. Parikh, W. Wu, and E. P. Xing, Robust Reverse Engineering of Dynamic Gene Networks under Sample Heterogeneity, Pacific Symposium on Biocomputing 2014 (PSB 2014).

2013

Q. Ho, J. Cipar, H. Cui, J.-K. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger and E. P. Xing, More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

S. Williamson, S. N. MacEachern and E. P. Xing, Restricting exchangeable nonparametric distributions, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material] ( arXiv:1209.1145)

J. Yin, Q. Ho and E. P. Xing, A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

C. Wang, X. Chen, A. Smola and E. P. Xing, Variance Reduction for Stochastic Gradient Optimization, Advances in Neural Information Processing Systems 27 (NIPS 2013). [Supplemental Material]

G. Kim and E. P. Xing, Discovering Pictorial Brand Associations from Large-Scale Online Image Data, Proceedings of The 7th ACM International Conference on Web Search and Data Mining (lsviscom2013). [software and project page] (Recipient of the BEST PAPER Award)

K Puniyani and E. P. Xing, GINI:From ISH images to Gene Interaction Networks, PLoS Computational Biology, 9(10): e1003227, 2013 [pdf preprint].

M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing and M. Sugiyama, High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso, Neural Computation, Vol. 26, No. 1, Pages 185-20, 2013. [pdf preprint] ( arXiv:1202.0515)

P. Xie and E. P. Xing, Integrating Document Clustering and Topic Modeling, Proceedings of the 29th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2013).

T. Bahadori, Y. Liu and E. P. Xing, Fast Structure Learning in Generalized Stochastic Processes with Latent Factors, Proceedings of The 19th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2013).

P. Xie and E. P. Xing, Multi-Modal Distance Metric Learning, 23rd International Joint Conference on Artificial Intelligence (IJCAL 2013).

A. Ahmed and E. P. Xing, Scalable Dynamic Nonparametric Bayesian Models of Content and Users, 23rd International Joint Conference on Artificial Intelligence (IJCAL 2013). [Invited to Sister Conference Best Papers Track, based on KDD12 best Ph.D. dissertation award]

J. Cipar, Q. Ho, J.-K. Kim, S. Lee, G. R. Ganger, G. Gibson, K. Keeton and E. P. Xing, Solving the straggler problem with bounded staleness, The 14th Workshop on Hot Topics in Operating Systems (HotOS XIV).

R.E. Curtis, S. Kim, J. L. Woolford, W. Xu and E. P. Xing, Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules, BMC Genomics 2013, vol. 14, no. 196, 2013. [pdf]

G. Kim and E. P. Xing, Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines, Proceedings of 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013). [software and project page][Supplemental Material]

B. Zhao and E. P. Xing, Sparse Output Coding for Large-scale Visual Recognition, Proceedings of 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013).

W. Wang, Y. Liang and E. P. Xing, Block Regularized Lasso for Multivariate Multi-Response Linear Regression, Proceedings of the 16th International Conference on Artifical Intelligence and Statistics (AISTAT 2013).

M. Kolar, H. Liu and E. P. Xing, Markov Network Estimation From Multi-attribute Data, The 30th International Conference on Machine Learningy (ICML 2013).

L. Song, M. Ishteva, A.P. Parikh, H. Park and E. P. Xing, Hierarchical Tensor Decomposition for Latent Tree Graphical Models, The 30th International Conference on Machine Learningy (ICML 2013).

R. Ranganath, C. Wang, D. Blei and E. P. Xing, An adaptive learning rate for stochastic variational inference, The 30th International Conference on Machine Learningy (ICML 2013).

A. Dubey, S. Williamson and E. P. Xing, Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models, The 30th International Conference on Machine Learningy (ICML 2013). [Supplemental Material]

K Puniyani and E. P. Xing, NP-MuScL: Unsupervised global prediction of interaction networks from multiple data sources, Proceedings of the Seventeenth Annual International Conference on Research in Computational Molecular Biology (RECOMB2013). [software]

A. Dubey, S. Williamson and E. P. Xing, A nonparametric mixture model for topic modeling over time, Proceedings of The Thirteenth SIAM International Conference on Data Mining (SDM2013). (arXiv:1208.4411, communicated 22 Aug 2012.)

G. Kim and E. P. Xing, Time-Sensitive Web Image Ranking and Retrieval via Dynamic Multi-Task Regression, Proceedings of The 6th ACM International Conference on Web Search and Data Mining (WSDM 2013). [software and project page]

2012

M. Kolar and E. P. Xing, Estimating Networks With Jumps, Electronic Journal of Statistics Vol. 6 (2012) 2069-2106. (arXiv:1012.3795, communicated 17 Dec 2010.)

Q. Ho, J. Yin and E. P. Xing, On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks, Advances in Neural Information Processing Systems 26 (NIPS 2012). [Supplemental Material] [software]

Q. Jiang, J. Zhu, M. Sun and E. P. Xing, Monte Carlo Methods for Maximum Margin Supervised Topic Models, Advances in Neural Information Processing Systems 26 (NIPS 2012). [Supplemental Material] [software]

K. Fukumasu, K. Eguchi and E. P. Xing, Symmetric Correspondence Topic Models for Multilingual Text Analysis, Advances in Neural Information Processing Systems 26 (NIPS 2012).

K. Puniyani and E. P. Xing, Inferring gene interaction networks from ISH images via kernelized graphical models, Proceeding of the 13th European Conference of Computer Vision (ECCV 2012).

A. Parikh, L. Song, M. Ishteva, G. Teodoru and E. P. Xing, A Spectral Algorithm for Latent Junction Trees, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2012). [Supplemental Material]

J. Zhu, A. Ahmed, and E. P. Xing, MedLDA: Maximum Margin Supervised Topic Models, Journal of Machine Learning Research 13 (2012) 2237-2278. ( arXiv:0912.5507)

Q. Ho, A. Parikh and E. P. Xing, A Multiscale Community Blockmodel for Network Exploration, Journal of American Statistical Association, volume 107, issue 499, 916-934, 2012. [pdf preprint]

K. Sohn, Z. Ghahramani and E. P. Xing, Robust Estimation of Local Genetic Ancestry in Admixed Populations Using a Non-parametric Bayesian Approach, Genetics, vol 191, no. 4, 2012. [pdf preprint] [Supplemental Material] [software]

R.E. Curtis, J. Xiang, A. Parikh, P. Kinnaird and E. P. Xing, Enabling dynamic network analysis through visualization in TVNViewer, BMC Bioinformatics, vol. 13, no. 204, 2012. [pdf] [software]

R.E. Curtis, A. Goyal and E. P. Xing, Enhancing the usability and performance of structured as- sociation mapping algorithms using automation, parallelization, and visualization in the GenAMap software system, BMC Genetics, vol. 13, no. 24, 2012. [pdf] [software]

J. Hu, Y. Liang and E. P. Xing, Nonparametric Decentralized Detection Based on Weighted Count Kernel, Proceedings of the 2012 IEEE International Symposium on Information Theory (ISIT 2012).

J. Yin, X. Chen and E. P. Xing, Group Sparse Additive Models, The 29th International Conference on Machine Learningy (ICML 2012).

M. Kolar and E. P. Xing, Consistent Covariance Selection From Data With Missing Values, The 29th International Conference on Machine Learningy (ICML 2012).

G. Kim, L. Fei-Fei and E. P. Xing, Web Image Prediction Using Multivariate Point Processes, Proceedings of The 18th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2012). [software and project page]

G. Kim and E. P. Xing, On Multiple Foreground Cosegmentation, Proceedings of 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012). [Supplemental Material] [software and project page]

S. Lee and E. P. Xing, Leveraging Input and Output Structures For Joint Mapping of Epistatic and Marginal eQTLs, the Twenties International Conference on Intelligence Systems for Molecular Biology (ISMB 2012). Bioinformatics (2012) 28 (12): i137-i146. [pdf]

Y. Zhang, D.-Y. Yeung and E. P. Xing, Supervised Probabilistic Robust Embedding with Sparse Noise, Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012).

N. Chen, J. Zhu, and E. P. Xing, Predictive Subspace Learning for Multi-view Data: a Large Margin Approach, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2012. [Supplemental Material]

S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Response Regression with Structured Sparsity, with applications to eQTL Mapping, Annals of Applied Statistics, Volume 6, Number 3 (2012), 1095-1117. [pdf]

X. Chen, Q. Lin, S. Kim, J. Carbonell and E. P. Xing, Smoothing Proximal Gradient Method for General Structured Sparse Regression, Annals of Applied Statistics, Volume 6, Number 2 (2012), 719-752. [pdf]

J. Eisenstein, D.H. Chau, A. Kittur, and E. P. Xing, TopicViz: Semantic Navigation of Document Collections, Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, Work-in-Progress Paper (CHI 2012).

Q. Ho, J. Eisenstein and E. P. Xing, Document Hierarchies from Text and Links, Proceedings of the International World Wide Web Conference (WWW 2012).

R.E. Curtis, J. Yin, P. Kinnaird and E. P. Xing, Finding Genome-Transcriptome-Phenome Association With Structured Association Mapping And Visualization In GenAMap, Pacific Symposium on Biocomputing 2012 (PSB 2012). [software and project page]

2011

L. Song, A. Parikh and, and E. P. Xing, Kernel Embeddings of Latent Tree Graphical Models, Advances in Neural Information Processing Systems 25 (NIPS 2011). [Supplemental Material]

J. Zhu, N. Chen and E. P. Xing, Infinite Latent SVM for Classification and Multi-task Learning, Advances in Neural Information Processing Systems 25 (NIPS 2011).

B. Zhao, L. Fei-Fei and E. P. Xing, Large-Scale Category Structure Aware Image Categorization, Advances in Neural Information Processing Systems 25 (NIPS 2011).

X. Chen, Q. Lin, S. Kim, J. Carbonell and E. P. Xing, Smoothing Proximal Gradient Method for General Structured Sparse Learning, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2011).

J. Zhu and E. P. Xing, Sparse Topical Coding, Proceedings of the 27th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2011).

G. Kim, E. P. Xing, L. Fei-Fei and T. Kanade, Distributed Cosegmentation via Submodular Optimization on Anisotropic Diffusion, Proceedings of 13th International Conference on Computer Vision (ICCV 2011).[Supplemental Material] [software and project page]

R.E. Curtis, P. Kinnaird and E. P. Xing, GenAMap: Visualization Strategies for Structured Association Mapping, 1st IEEE Symposium on Biological Data Visualization, 2011. [software]

R.E. Curtis, A. Yuen, L. Song, A. Goyal; and E. P. Xing, TVNViewer: An interactive visualization tool for exploring networks that change over time or space, Bioinformatics 2011. [software]

J. Zhu, N. Lao, N. Chen and E. P. Xing, Conditional Topical Coding: an Efficient Topic Model Conditioned on Rich Features, Proceedings of The 17th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2011).

J. Zhu, N. Chen and E. P. Xing, Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines, The 28th International Conference on Machine Learningy (ICML 2011).

A.F.T. Martins, M.A.T. Figueiredo, P.M.Q. Aguiar, N.A. Smith and E. P. Xing, An Augmented Lagrangian Approach to Constrained MAP Inference, The 28th International Conference on Machine Learningy (ICML 2011).

A. Parikh, L. Song and E. P. Xing, A Spectral Algorithm for Latent Tree Graphical Models, The 28th International Conference on Machine Learningy (ICML 2011).

H. Kamisetty, E. P. Xing, C. J. Langmead, Approximating Correlated Equilibria using Relaxations on the Marginal Polytope, The 28th International Conference on Machine Learningy (ICML 2011).

J. Eisenstein, A. Ahmed and E. P. Xing, Sparse Additive Generative Models of Text, The 28th International Conference on Machine Learningy (ICML 2011).

A. Parikh*, W. Wu* (equal contributing 1st author), R. Curtis and E. P. Xing, Reverse Engineering Tree-Evolving Gene Networks Underlying Developing Biological Lineages, the Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011). Bioinformatics, Volume 27, Issue 13Pp. i196-i204. [pdf] [software] (Recipient of the BEST PAPER Award)

S. Shringarpure, D. Won and E. P. Xing, StructHDP: Automatic inference of number of clusters from admixed genotype data, the Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011). Bioinformatics, Volume 27, Issue 13Pp. i324-i332. [pdf] [software]

Q. Ho, A. Parikh, L. Song and E. P. Xing, Multiscale Community Blockmodel for Network Exploration, Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

Q. Ho, L. Song and E. P. Xing, Evolving Cluster Mixed-Membership Blockmodel for Time-Evolving Networks, Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

A.F.T. Martins, M.A.T. Figueiredo, P.M.Q. Aguiar, N.A. Smith and E. P. Xing, Online Learning of Structured Predictors with Multiple Kernels, Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

M. Kolar and E. P. Xing, On Time Varying Undirected Graphs, Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

A. Ahmed, Q. Ho, C. H. Teo, J. Eisenstein, A. Smola and E. P. Xing, Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text, Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTAT 2011). [Supplemental Material]

B. Zhao, L. Fei-Fei and E. P. Xing, Online Detection of Unusual Events in Videos via Dynamic Sparse Coding, Proceeding of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011). Watch a movie of our detection demo on youtube!

J. Eisenstein, N. Smith and E. P. Xing, Discovering Sociolinguistic Associations with Structured Sparsity, The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).

A. Ahmed, Q. Ho, J. Eisenstein, E. P. Xing, A. Smola and C. H. Teo, Unified Analysis of Streaming News, Proceedings of the International World Wide Web Conference (WWW 2011).

S. Kim and E. P. Xing, Exploiting Genome Structure in Association Analysis, Journal of Computational Biology, Volume: 18,Pages: 1-16, 2011. [Journal Link]

2010

S. Lee, J. Zhu, and E. P. Xing, Adaptive Multi-Task Lasso: with application to eQTL detection, Advances in Neural Information Processing Systems 24 (NIPS 2010).

N. Chen, J. Zhu, and E. P. Xing, Predictive Subspace Learning for Multi-view Data: a Large Margin Approach, Advances in Neural Information Processing Systems 24 (NIPS 2010).

J. Zhu, J. Li, L. Fei-Fei, and E. P. Xing, Large Margin Learning of Upstream Scene Understanding Models, Advances in Neural Information Processing Systems 24 (NIPS 2010).

J. Li, H. Su, E. P. Xing, and L. Fei-Fei, Object Bank: A High-Level Image Representation for Scene Classification and Semantic Feature Sparsification, Advances in Neural Information Processing Systems 24 (NIPS 2010).

Q. Ho, A.P. Parikh, L. Song and E. P. Xing, Infinite Hierarchical MMSB Model for Nested Communities/Groups in Social Networks, Manuscript, arXiv:1010.1868, communicated Oct 2010.

A.F.T. Martins, M.A.T. Figueiredo, P.M.Q. Aguiar, N.A. Smith and E. P. Xing, Online Multiple Kernel Learning for Structured Prediction, Manuscript, arXiv:1005.2770, communicated Oct 2010.

A. Ahmed and E. P. Xing, Staying Informed: Supervised and Semi-Supervised Multi-view Topical Analysis of Ideological Perspective, 2010 Conference on Empirical Methods on Natural Language Processing (EMNLP 2010).

J. Eisenstein, B. O'Connor, N. A. Smith, and E. P. Xing, A Latent Variable Model for Geographic Lexical Variation, 2010 Conference on Empirical Methods on Natural Language Processing (EMNLP 2010).

A. F. T. Martins, N. A. Smith, E. P. Xing, M. Figueiredo, and P. Aguiar, Turbo Parsers: Dependency Parsing by Approximate Variational Inference, 2010 Conference on Empirical Methods on Natural Language Processing (EMNLP 2010).

S. Hanneke, W. Fu and E. P. Xing, Discrete Temporal Models of Social Networks, Electronic Journal of Statistics Vol. 4 (2010) 585-605. (arXiv:0908.1258, communicated August 2009.)

A. Ahmed and E. P. Xing, Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream, Proceedings of the 26th International Conference on Conference on Uncertainty in Artificial Intelli- gence (UAI 2010).

B. Zhao, L. Fei-Fei and E. P. Xing, Image Segmentation with Topic Random Fields, Proceeding of the 12th European Conference of Computer Vision (ECCV 2010).

G. Kim, E. P. Xing, and A. Torralba, Modeling and Analysis of Dynamic Behaviors of Web Image Collections, Proceeding of the 12th European Conference of Computer Vision (ECCV 2010).

J. Zhu, N. Lao and E. P. Xing, Grafting-Light: Fast, Incremental Feature Selection and Structure Learning of Markov Random Fields, Proceedings of The 16th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2010).

X. Chen, Q. Lin, S. Kim, J. Pena, J. G. Carbonell and E. P. Xing, An Efficient Proximal Gradient Method for General Structured Sparse Learning, Manuscript, arXiv:1005.4717, communicated May 2010.

X. Chen, S. Kim, Q. Lin, J. G. Carbonell and E. P. Xing, Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso, Manuscript, arXiv:1005.3579, communicated May 2010.

M. Kolar, A. Parikh and E. P. Xing, On Sparse Nonparametric Conditional Covariance Selection, The 27th International Conference on Machine Learningy (ICML 2010).

S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity, The 27th International Conference on Machine Learningy (ICML 2010).

J. Zhu and E. P. Xing, Conditional Topic Random Fields, The 27th International Conference on Machine Learningy (ICML 2010).

K. Puniyani, S. Kim and E. P. Xing, Multi-Population GWA Mapping via Multi-Task Regularized Regression, the Eighteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2010). Bioinformatics 2010 26(12):i208-i216.

K. Puniyani, C. Faloutsos and E. P. Xing, SPEX2 : Automated Concise Extraction of Spatial Gene Expression Patterns from Fly Embryo ISH Images, the Eighteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2010). Bioinformatics 2010 26(12):i47-i56.

M. Kolar and E. P. Xing, Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit, Proceedings of the 13th International Conference on Artifical Intelligence and Statistics (AISTAT 2010). [Supplemental Material]

S. Lee, E. P. Xing and M. Brudno, MoGUL: Detecting Common Insertions and Deletions in a Population, Proceedings of the Fourteenth Annual International Conference on Research in Computational Molecular Biology (RECOMB2010). [software]

2009

J. Zhu, A. Ahmed, and E. P. Xing, MedLDA: A General Framework of Maximum Margin Supervised Topic Models, Manuscript, arXiv:0912.5507, communicated Dec 2009.

E.P. Xing, W. Fu, and L. Song, A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography, Annals of Applied Statistics, Vol. 4, No. 2, 535 - 566, 2010. (earlier version appeared in arXiv:0901.0135)

M. Kolar, L. Song, A. Ahmed, and E. P. Xing, Estimating Time-Varying Networks , Annals of Applied Statistics, Vol. 4, No. 1, 94 - 123, 2010. (earlier version appeared in arXiv:0812.5087)

M. Kolar, L. Song and E. P. Xing, Sparsistent Learning of Varying-coefficient Models with Structural Changes, Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009). [appendix available soon]

L. Song, M. Kolar and E. P. Xing, Time-Varying Dynamic Bayesian Networks, Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009). [appendix available soon]

X. Yang, S. Kim and E. P. Xing, Heterogeneous Multitask Learning with Joint Sparsity Constraints, Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009).

S. Kim and E. P. Xing, Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity, Manuscript, arXiv:0909.1373, communicated September 2009.

M. Kolar and E. P. Xing, Sparsistent Estimation of Time-Varying Discrete Markov Random Fields, Manuscript, arXiv:0907.2337, communicated July 2009.

J. Zhu and E. P. Xing, Maximum Entropy Discrimination Markov Networks, Journal of Machine Learning Research, 10(Nov):2531-2569, 2009.

S. Kim and E. P. Xing, Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network, PLoS Genetics 5(8): e1000587, 2009. [pdf] [software]

A. Ahmed and E. P. Xing, TESLA: Recovering Time-Varying Networks of Dependencies in Social and Biological Studies, Proc. Natl. Acad. Sci. USA, vol. 106, no. 29, 11878-11883. [pdf] [supplemental material] [software]

J. Zhu, A. Ahmed and E. P. Xing, MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification, The 26th International Conference on Machine Learningy (ICML 2009).

J. Zhu and E. P. Xing, On Primal and Dual Sparsity of Markov Networks, The 26th International Conference on Machine Learningy (ICML 2009).

W. Fu, L. Song and E. P. Xing, Dynamic Mixed Membership Block Model for Evolving Networks, The 26th International Conference on Machine Learningy (ICML 2009).

A. Martins, N. Smith and E. P. Xing, Polyhedral Outer Approximations with Application to Natural Language Parsing, The 26th International Conference on Machine Learningy (ICML 2009).

A. Martins, N. Smith and E. P. Xing, Concise Integer Linear Programming Formulations for Dependency Parsing, The 47th Annual Meeting of the Association for Computational Linguistics (ACL 2009). (Recipient of the BEST PAPER Award)

J. Zhu, E.P. Xing and B. Zhang, Primal Sparse Max-Margin Markov Networks, The 15th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2009).

A. Ahmed, E.P. Xing, W. Cohen, and R. Murphy, Structured Correspondence Topic Models for Mining Captioned Figures in Biological Literature, The 15th ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2009).

S. Kim, K. Sohn and E. P. Xing, A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network, The Seventeenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). Bioinformatics 2009 25(12):i204-i212.

L. Song, M. Kolar and E. P. Xing, KELLER: Estimating Time-Evolving Interactions Between Genes, The Seventeenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). Bioinformatics 2009 25(12):i128-i136. [software]

W. Fu, P. Ray and E. P. Xing, DISCOVER: A feature-based discriminative method for motif search in complex genomes, The Seventeenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). Bioinformatics 2009 25(12):i321-i329.[software]

S. Shringarpure and E. P. Xing, mStruct: Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations, Genetics, Vol 182, issue 2, 2009. [pdf] [software]

J. A. Howrylak, T. Dolinay, L. Lucht, Z. Wang, D. C. Christiani, J. M. Sethi1, E. P. Xing, M. P. Donahoe and A. M. K. Choi, Discovery of the gene signature for acute lung injury in patients with sepsis, Physiol. Genomics 37: 133-139, 2009.

S. Hanneke and E. P. Xing, Network Completion and Survey Sampling, Proceedings of the 12th International Conference on Artifical Intelligence and Statistics (AISTAT 2009)

K. Sohn and E. P. Xing, A Hierarchical Dirichlet Process Mixture Model For Haplotype Reconstruction From Multi-Population Data, Annals of Applied Statistics, Vol. 3, no. 2, 791-821, 2009. [pdf] [software]

A. Martins, N. A. Smith, E. P. Xing, M. Figueiredo, and P. Aguiar, Nonextensive Information Theoretic Kernels on Measures, Journal of Machine Learning Research, 2009, Vol 10, pp935-975.