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2018


[136] Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, and Le Song. Learning temporal point processes via reinforcement learning. In Advances in Neural Information Processing Systems (NIPS), 2018. [ bib ]
[135] Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song. Learning towards minimum hyperspherical energy. In Advances in Neural Information Processing Systems (NIPS), 2018. [ bib ]
[134] Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, and Le Song. Coupled variational bayes via optimization embedding. In Advances in Neural Information Processing Systems (NIPS), 2018. [ bib ]
[133] Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, and Le Song. Learning loop invariants for program verification. In Advances in Neural Information Processing Systems (NIPS), 2018. [ bib ]
[132] Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, and Le Song. Sbeed: Convergent reinforcement learning with nonlinear function approximation. In International Conference on Machine Learning (ICML), 2018. [ bib | http ]
[131] Hajun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, and Le Song. Learning steady states of iterative algorithms over graphs. In International Conference on Machine Learning (ICML), 2018. [ bib | .html ]
[130] Weiyang Liu, Bo Dai, Xingguo Li, James Rehg, and Le Song. Towards black-box iterative machine teaching. In International Conference on Machine Learning (ICML), 2018. [ bib | http ]
[129] Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. Adversarial attack on graph structured data. In International Conference on Machine Learning (ICML), 2018. [ bib | http ]
[128] Jianfei Chen, Jun Zhu, and Le Song. Stochastic training of graph convolutional networks. In International Conference on Machine Learning (ICML), 2018. [ bib | http ]
[127] Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. Learning to explain: An information-theoretic perspective on model interpretation. In International Conference on Machine Learning (ICML), 2018. [ bib | http ]
[126] Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, and Haesun Park. Multi-scale nystrom method. In Artificial Intelligence and Statistics (AISTATS), 2018. [ bib | .html ]
[125] Yichen Wang, Evangelos Theodorou, Apurv Verma, and Le Song. A stochastic differential equation framework for guiding online user activities in closed loop. In Artificial Intelligence and Statistics (AISTATS), 2018. [ bib | http ]
[124] Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song. Syntax-directed variational autoencoder for structured data. In International Conference on Learning Representations (ICLR), 2018. [ bib | http ]
[123] Bo Dai, Albert Shaw, Niao He, Lihong Li, and Le Song. Boosting the actor with dual critic. In International Conference on Learning Representations (ICLR), 2018. [ bib | http ]
[122] Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia. Iterative learning with open-set noisy labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [ bib | http ]
[121] Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James Rehg, and Le Song. Decoupled networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [ bib | http ]
[120] Y. Zhang, H. Dai, Z. Kozareva, A. Smola, and L. Song. Variational reasoning for question answering with knowledge graph. In AAAI Conference on Artificial Intelligence (AAAI), 2018. [ bib | http ]
[119] K. Kawaguchi, B. Xie, V. Vikas, and L. Song. Deep semi-random features for nonlinear function approximation. In AAAI Conference on Artificial Intelligence (AAAI), 2018. [ bib | http ]
[118] S. Xiao, M. Farajtabar, X. Ye, J. Yan, L. Song, and H. Zha. Learning conditional generative models for temporal point processes. In AAAI Conference on Artificial Intelligence (AAAI), 2018. [ bib | http ]


2017


[117] H. Dai, E. Khalil, Y. Zhang, B. Dilkina, and L.Song. Learning combinatorial optimization algorithms over graphs. In Neural Information Processing Systems (NIPS), 2017. [ bib | http ]
[116] X. Xu, C. Liu, Q. Feng, H. Yin, L. Song, and D. Song. Neural network-based graph embedding for cross-platform binary code similarity detection. In ACM Conference on Computer and Communications Security (CCS), 2017. [ bib | http ]
[115] S. Xiao, M. Farajtabar, X. Ye, J. Yan, L. Song, and H. Zha. Wasserstein learning of deep generative point process models. In Neural Information Processing Systems (NIPS), 2017. [ bib | http ]
[114] W. Liu, Y. Zhang, X. Li, Z. Yu, B. Dai, T. Zhao, and L. Song. Deep hyperspherical learning of convolution neural networks. In Neural Information Processing Systems (NIPS), 2017. [ bib | http ]
[113] L. Song, S. Vempala, J. Wilmes, and B. Xie. On the complexity of learning neural networks. In Neural Information Processing Systems (NIPS), 2017. [ bib | http ]
[112] Y. Wang, X. Ye, H. Zha, and L. Song. Predicting user activity level in point process models with mass transport equation. In Neural Information Processing Systems (NIPS), 2017. [ bib | http ]
[111] L. Song, H. Liu, A. Parikh, and E. Xing. Nonparametric latent tree graphical models: Inference, estimation, and structure learning. Journal of Machine Learning Researches (JMLR), 2017. [ bib | .pdf ]
[110] M. Farajtabar, Y. Wang, M. Rodriguez, S. Li, H. Zha, and L. Song. Coevolve: A joint point process model for information diffusion and network co-evolution. Journal of Machine Learning Researches (JMLR), 2017. [ bib | http ]
[109] Hanjun Dai, Ramzan Umarov, Hiroyuki Kuwahara, Yu Li, Le Song, and Xin Gao. Sequence2vec: A novel embedding approach for modeling transcription factor binding affinity landscape. Bioinformatics, page 480, 2017. [ bib | http ]
[108] Yichen Wang, Grady Williams, Evangelos Theodorou, and Le Song. Variational policy for guiding point processes. In International Conference on Machine Learning (ICML), 2017. [ bib | http ]
[107] Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, and Le Song. Stochastic generative hashing. In International Conference on Machine Learning (ICML), 2017. [ bib | http ]
[106] Rakshit Trivedi, Hajun Dai, Yichen Wang, and Le Song. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In International Conference on Machine Learning (ICML), 2017. [ bib | http ]
[105] Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda Smith, James Rehg, and Le Song. Iterative machine teaching. In International Conference on Machine Learning (ICML), 2017. [ bib | http ]
[104] Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, and Hongyuan Zha. Fake news mitigation via point process based intervention. In International Conference on Machine Learning (ICML), 2017. [ bib | http ]
[103] S. Li, Y. Xie, M. Farajtabar, A. Verma, and L. Song. Detecting weak changes in dynamic events over networks. IEEE Transactions on Signal and Information Processing over Networks, 2017. [ bib | http ]
[102] B. Tabibian, I. Valera, M. Farajtabar, L. Song, B. Schölkopf, and M. Rodriguez. Distilling information reliability and source trustworthiness from digital traces. In World Wide Web (WWW), 2017. [ bib | http ]
[101] H. Dai, Y. Wang, R. Trivedi, and L. Song. Recurrent coevolutionary feature embedding processes for recommendation. In Recsys Workshop on Deep Learning for Recommendation Systems, 2017. [ bib | http ]
[100] Y. Wang, X. Ye, , H. Zha H. Zhou, and L. Song. Fokker-planck inference machines: linking microscopic event history to macroscopic prediction. In Artificial Intelligence and Statistics, 2017. [ bib | .pdf ]
[99] B. Dai, N. He, Y. Pan, B. Boots, and L. Song. Learning from conditional distributions via dual embeddings. In Artificial Intelligence and Statistics, 2017. [ bib | http ]
[98] B. Xie, Y. Liang, and L. Song. Diversity leads to generalization in neural networks. In Artificial Intelligence and Statistics, 2017. [ bib | http ]


2016


[97] Mehrdad Farajtabar, Xiaojing Ye, Sahar Harati, Le Song, and Hongyuan Zha. Multistage campaigning in social networks. In Neural Information Processing Systems (NIPS), 2016. [ bib | http ]
[96] Y. Wang, N. Du, R. Trivedi, and L. Song. Coevolutionary latent feature processes for continuous-time user-item interactions. In Neural Information Processing Systems (NIPS), 2016. [ bib | .pdf ]
[95] M. Karimi, E. Tavakoli, M. Farajtabar, L. Song, and M. Rodriguez. Smart broadcasting: Do you want to be seen? In Knowledge Discovery and Data Mining (KDD), 2016. [ bib | .pdf ]
[94] N. Du, H. Dai, R. Trivedi, U. Upadhyay, M. Rodriguez, and L. Song. Recurrent marked temporal point process. In Knowledge Discovery and Data Mining (KDD), 2016. [ bib | .pdf ]
[93] Y. Wang, B. Xie, and L. Song. Isotonic hawkes processes. In International Conference on Machine Learning (ICML), 2016. [ bib | .pdf ]
[92] S. Li, Y. Xie, M Farajtabar, and L. Song. Detecting weak changes in dynamic events over networks. In Arxiv, 2016. [ bib | http ]
[91] Y. Wang, E. Theodorou, A. Verma, and L. Song. Steering opinion dynamics in information diffusion networks. In Arxiv, 2016. [ bib | http ]
[90] H. Dai, B. Dai, and L. Song. Discriminative embeddings of latent variable models for structured data. In International Conference on Machine Learning (ICML), 2016. [ bib | .pdf ]
[89] N. Du, Y. Liang, M. Balcan, M. Rodriguez, H. Zha, and L. Song. Scalable influence maximization for multiple products. In Journal of Machine Learning Research (JMLR), 2016. [ bib | http ]
[88] M. Balcan, Y. Liang, L. Song, D. Woodruff, and B. Xie. Communication efficient distributed kernel principal component analysis. In Knowledge Discovery and Data Mining (KDD), 2016. [ bib | http ]
[87] Y. Nishiyama, A. Afsharinejad, B. Boots, and L. Song. Nonparametric kernel bayes smoother. In Artificial Intelligence and Statistics (AISTATS), 2016. [ bib | http ]
[86] B. Dai, N. He, H. Dai, and L. Song. Scalable bayesian inference via particle mirror descent. In Artificial Intelligence and Statistics (AISTATS), 2016. [ bib | http ]
[85] E. Khalil, P. Le Bodic, L. Song, G. Nemhauser, and B. Dilkina. Learning to branch in mixed integer programming. In AAAI Conference on Artificial Intelligence, 2016. [ bib | .pdf ]
[84] M. Rodriguez, L. Song, N. Du, H. Zha, and B. Schölkopf. Influence estimation and maximization in continuous time diffusion networks. ACM Transactions on Information System, 2016. [ bib | http ]


2015


[83] Y. Liu, L. Song, F. Li, S. Li, and J. Rehg. Efficient continuous-time hidden markov model for disease modeling. In Neural Information Processing Systems (NIPS), 2015. [ bib | www: ]
[82] N. Du, Y. Wang, N. He, and L. Song. Time-sensitive recommendation. In Neural Information Processing Systems (NIPS), 2015. [ bib | .pdf ]
[81] E. Choi, N. Du, R. Chen, L. Song, and J. Sun. Constructing disease network and temporal progression model via context-sensitive hawkes process. In International Conference on Data Mining (ICDM), 2015. [ bib | .pdf ]
[80] Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. Smola, L. Song, and Z. Wang. Deep fried convnets. In International Conference on Computer Vision (ICCV), 2015. [ bib | http ]
[79] S. Li, Y. Xie, H. Dai, and L. Song. M-statistic for kernel change-point detection. In Neural Information Processing Systems (NIPS), 2015. [ bib | http ]
[78] M. Farajtabar, Y. Wang, M. Rodriguez, S. Li, H. Zha, and L. Song. Coevolve: A joint point process model for information diffusion and network co-evolution. In Neural Information Processing Systems (NIPS), 2015. [ bib | http ]
[77] N. Du, M. Farajtabar, A. Ahmed, A. Smola, and L. Song. Dirichlet-hawkes processes with applications to clustering continuous-time document streams. In Knowledge Discovery and Data Mining (KDD), 2015. [ bib | .pdf ]
[76] A. Shaban, M. Farajtabar, B. Xie, L. Song, and B. Boots. Learning latent variable models via method of moments and exterior point optimization. In Uncerntainty in Artificial Intelligence (UAI), 2015. [ bib | .pdf ]
[75] Y. Liang B. Xie and L. Song. Scale up nonlinear component analysis with doubly stochastic gradients. In Neural Information Processing Systems (NIPS), 2015. [ bib | .pdf ]
[74] M. Rodriguez, L. Song, N. Daneshmand, and B. Schölkpof. Estimating diffusion network structure: Recovery conditions, sample complexity, and a soft-thresholding algorithm. In Journal of Machine Learning Researches (JMLR), 2015. [ bib | .pdf ]
[73] M. Farajtabar, N. Du, M. Zamani, M. Rodriguez, and L. Song. Back to the past: Source identification in diffusion networks. In Artificial Intelligence and Statistics (AISTATS), 2015. [ bib | .pdf ]
[72] Z. Yang, A. Smola, L. Song, and A. Wilson. A la carte -- learning fast kernels. In Artificial Intelligence and Statistics (AISTATS), 2015. [ bib | .pdf ]
[71] Y. You, J. Demmel, K. Czechowski, L. Song, and R. Vuduc. Ca-svm: Communication-avoiding support vector machines on clusters. In IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2015. [ bib | .pdf ]
[70] T. Long, M. Farajtabar, L. Song, and H. Zha. Netcodec: Community detection from individual activities. In SIAM International Conference on Data Mining (SDM), 2015. [ bib | .pdf ]


2014


[69] M. Balcan, C. Berlind, A. Blum, E. Cohen, K. Patnaik, and L. Song. Active learning and best-response dynamics. In Neural Information Processing Systems (NIPS), 2014. [ bib | .pdf ]
[68] N. Du, Y. Liang, M. Balcan, and L. Song. Learning time-varying coverage functions. In Neural Information Processing Systems (NIPS), 2014. [ bib | .pdf ]
[67] B. Dai, B. Xie, N. He, Y. Liang, A. Raj, M. Balcan, and L. Song. Scalable kernel methods via doubly stochastic gradients. In Neural Information Processing Systems (NIPS), 2014. [ bib | .pdf ]
[66] M. Farajtabar, N. Du, M. Rodriguez, I. Valera, H. Zha, and L. Song. Shaping social activity by incentivizing users. In Neural Information Processing Systems (NIPS), 2014. [ bib | .pdf ]
[65] M. Rodriguez, L. Song, and B. Scholkpof. Finding good cascade sampling processes for the network inference problem (open problem). In Conference on Learning Theory (COLT), 2014. [ bib | .pdf ]
[64] E. Khalil, B. Dilkina, and L. Song. Scalable diffusion-aware optimization of network topology. In Knowledge Discovery and Data Mining (KDD), 2014. [ bib | .pdf ]
[63] A. Agarwal, S. Kakade, N. Karampatziakis, L. Song, and G. Valiant. Least squares revisited: Scalable approaches for multi-class prediction. In International Conference on Machine Learning (ICML), 2014. [ bib | .pdf ]
[62] L. Song, A. Anamdakumar, B. Dai, and B. Xie. Nonparametric estimation of multi-view latent variable models. In International Conference on Machine Learning (ICML), 2014. [ bib | .pdf ]
[61] N. Daneshmand, M. Rodriguez, L. Song, and B. Scholkpof. Estimating diffusion network structure: Recovery conditions, sample complexity, and a soft-thresholding algorithm. In International Conference on Machine Learning (ICML), 2014. [ bib | .pdf ]
[60] N. Du, Y. Liang, N. Balcan, and L. Song. Influence function learning in information diffusion networks. In International Conference on Machine Learning (ICML), 2014. [ bib | .pdf ]


2013


[59] L. Song and B. Dai. Robust low rank kernel embedding of multivariate distributions. In Neural Information Processing Systems (NIPS), volume 27, 2013. [ bib | .pdf ]
[58] N. Du, L. Song, M. Rodriguez, and H. Zha. Scalable influence estimation in continuous time diffusion networks. In Neural Information Processing Systems (NIPS), volume 27, 2013. [ bib | .pdf ]
[57] B. Xie, B. Jankovic, V. Bajic, L. Song, and X. Gao. Polya motif prediction using spectral latent features from human dna sequences. In ISMB, 2013. [ bib | .pdf ]
[56] K. Zhou, H. Zha, and L. Song. Learning triggering kernels for multi-dimensional hawkes processes. In International Conference on Machine Learning (ICML), 2013. [ bib | .pdf ]
[55] L. Song, M. Ishteva, H. Park, A. Parikh, and E. Xing. Hierarchical tensor decomposition of latent tree graphical models. In International Conference on Machine Learning (ICML), 2013. [ bib | .pdf ]
[54] M. Ishteva, L. Song, and H. Park. Unfolding latent tree structures using 4th order tensors. In International Conference on Machine Learning (ICML), 2013. [ bib | .pdf ]
[53] L. Song, A. Gretton, and K. Fukumizu. Kernel embeddings of conditional distributions. IEEE Signal Processing Magazine, 2013. [ bib | .pdf ]
[52] K. Zhou, H. Zha, and L. Song. Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes. In Artificial Intelligence and Statistics (AISTATS), 2013. [ bib | .pdf ]
[51] N. Du, L. Song, H. Woo, and H. Zha. Uncover topic-sensitive information diffusion networks. In Artificial Intelligence and Statistics (AISTATS), 2013. [ bib | .pdf ]


2012


[50] M. Ishteva, H. Park, and L. Song. Unfolding latent tree structures using 4th order tensors. arxiv.org/pdf/1210.1258, 2012. [ bib | .pdf ]
[49] K. Fukumizu, L. Song, and A. Gretton. Kernel bayes rule: Bayesian inference with positive definite kernels. In Journal of Machine Learning Research (JMLR), 2012. [ bib | .pdf ]
[48] L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt. Feature selection via dependence maximization. Journal of Machine Learning Research (JMLR), 13:1393--1434, 2012. [ bib | .pdf ]
[47] N. Du, L. Song, A. Smola, and M. Yuan. Estimating heterogeneous social influence. In Neural Information Processing Systems (NIPS), volume 26, 2012. [ bib | .pdf ]
[46] A.P. Parikh, L. Song, M. Ishteva, G. Teodoru, and E.P. Xing. A spectral algorithm for latent junction trees. In Conference on Uncertainty in Artificial Intelligence, 2012. [ bib | .pdf ]


2011


[45] A. Parikh, L. Song, and E. P. Xing. A spectral algorithm for latent tree graphical models. In International Conference on Machine Learning (ICML), 2011. [ bib | .pdf ]
[44] L. Song, A. Parikh, and E. Xing. Kernel embeddings of latent tree graphical models. In Neural Information Processing Systems (NIPS), 2011. [ bib | .pdf ]
[43] K. Fukumizu, L. Song, and A. Gretton. Kernel bayes rule. In Neural Information Processing Systems (NIPS), 2011. [ bib | .pdf ]
[42] A. Anandkumar, K. Chaudhuri, D. Hsu, S. Kakade, L. Song, and T. Zhang. Spectral methods for learning multivariate latent tree structure. In Neural Information Processing Systems (NIPS), 2011. [ bib | .pdf ]
[41] R. Curtis, A. Yuen, L. Song, A. Goyal, and E. Xing. Tvnviewer: An interactive visualization tool for exploring networks that change over time and space. Bioinformatics, 27(13):1880--1, 2011. [ bib | .pdf ]
[40] L. Song, A. Gretton, D. Bickson, Y. Low, and C. Guestrin. Kernel belief propagation. In International Conference on Artifical Intelligence and Statistics (AISTATS), 2011. [ bib | .pdf ]
[39] Q. Ho, L. Song, and E. Xing. Evolving cluster mixed-membership blockmodel for time-evolving networks. In International Conference on Artifical Intelligence and Statistics (AISTATS), 2011. [ bib | .pdf ]
[38] Q. Ho, A. Parikh, L. Song, and E. Xing. Multiscale community blockmodel for network exploration. In International Conference on Artifical Intelligence and Statistics (AISTATS), 2011. [ bib | .pdf ]


<2010


[37] L. Song, B. Boots, S. Siddiqi, G. Gordon, and A. Smola. Hilbert space embeddings of hidden markov models. In International Conference on Machine Learning (ICML), 2010. [ bib | .pdf ]
[36] L. Song, A. Gretton, and C. Guestrin. Nonparametric tree graphical models. In Artificial Intelligence and Statistics (AISTATS), 2010. [ bib | .pdf ]
[35] T. Huang, L. Song, and J. Schneider. Learning nonlinear dynamic models from non-sequenced data. In Artificial Intelligence and Statistics (AISTATS), 2010. [ bib | .pdf ]
[34] N. Quadrianto, A. Smola, L. Song, and T. Tuytelaars. Kernelized sorting. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010. [ bib | .pdf ]
[33] M. Kolar, L. Song, A. Ahmed, and E. Xing. Estimating time-varying networks. Annals of Applied Statistics, 2010. [ bib | .pdf ]
[32] E. Xing, W. Fu, and L. Song. A state-space mixed membership blockmodel for dynamic network tomography. Annals of Applied Statistics, 2010. [ bib | .pdf ]
[31] L. Song, M. Kolar, and E. Xing. Time-varying dynamic bayesian networks. In Advances in Neural Information Processing Systems 22 (NIPS), 2010. [ bib | .pdf ]
[30] M. Kolar, L. Song, and E. Xing. Sparsistent learning of varying-coefficient models with structural changes. In Advances in Neural Information Processing Systems 22 (NIPS), 2010. [ bib | .pdf ]
[29] L. Song, J. Huang, A. Smola, and K. Fukumizu. Hilbert space embeddings of conditional distributions. In International Conference on Machine Learning, 2009. [ bib | .pdf ]
[28] W. Fu, L. Song, and E. Xing. Dynamic mixed membership blockmodel for evolving networks. In International Conference on Machine Learning, 2009. [ bib | .pdf ]
[27] L. Song, M. Kolar, and E. Xing. Keller: Estimating time-evolving interactions between genes. Bioinformatics (ISMB), 25(12):i128--i136, 2009. [ bib | .pdf ]
[26] A. Smola, L. Song, and C. Teo. Relative novelty detection. In AI and Statistics, 2009. [ bib | .pdf ]
[25] M. Thoma, H. Cheng, A. Gretton, J. Han, H. Kriegel, A. Smola, L. Song, P. Yu, X. Yan, and K. Borgwardt. Near-optimal supervised feature selection among frequent subgraphs. In SIAM International Conference on Data Mining (SDM), 2009. [ bib | .pdf ]
[24] X. Zhang, L. Song, A. Gretton, and A. Smola. Kernel measures of independence for non-iid data. In Advances in Neural Information Processing Systems 21, 2009. [ bib | .pdf ]
[23] N. Quadrianto, L. Song, and A. Smola. Kernelized sorting. In Advances in Neural Information Processing Systems 21, 2009. [ bib | .pdf ]
[22] L. Song. Learning via hilbert space embedding of distributions. In PhD Thesis, 2008. [ bib | .pdf ]
[21] K. Borgwardt, X. Yan, M. Thoma, H. Cheng, A. Gretton, L. Song, A. Smola, J. Han, P. Yu, and H. Kriegel. Combining near-optimal feature selection with gspan. In Workshop on Mining and Learning with Graphs, 2008. [ bib | .pdf ]
[20] L. Song, X. Zhang, A. Smola, A. Gretton, and B. Schölkopf. Tailoring density estimation via reproducing kernel moment matching. In International Conference on Machine Learning, 2008. [ bib | .pdf ]
[19] L. Song, A. Smola, K. Borgwardt, and A. Gretton. Colored maximum variance unfolding. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008. [ bib | .pdf ]
[18] A. Gretton, K. Fukumizu, C. Teo, L. Song, B. Schölkopf, and A. Smola. A kernel statistical test of independence. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008. [ bib | .pdf ]
[17] L. Williams, J. Gatt, S. Kuan, C. Dobson-Stone, D. Palmer, R. Paul, L. Song, P. Costa, P. Schofield, and E. Gordon. A polymorphism of the maoa gene is associated with emotional brain markers and personality traits on an antisocial index. Nature Neuropsychopharmacology, 34:1797--1809, 2009. [ bib | .html ]
[16] A. Smola, A. Gretton, L. Song, and B. Schölkopf. A hilbert space embedding for distributions. In Algorithmic Learning Theory. Springer, 2007. Invited paper. [ bib | .pdf ]
[15] L. Song, J. Bedo, K. Borgwardt, A. Gretton, and A. Smola. Gene selection via the bahsic family of algorithms. Bioinformatics (ISMB), 23(13):i490--i498, 2007. [ bib | .pdf ]
[14] L. Song, A. Smola, A. Gretton, K. Borgwardt, and J. Bedo. Supervised feature selection via dependence estimation. In C. Sammut and Z. Ghahramani, editors, International Conference on Machine Learning, 2007. [ bib | .pdf ]
[13] L. Song, A. Smola, A. Gretton, and K. Borgwardt. A dependence maximization view of clustering. In International Conference on Machine Learning, 2007. [ bib | .pdf ]
[12] L. Williams, D. Palmer, B. Liddell, L. Song, and E. Gordon. The when and where of perceiving signals of threat versus non-threat. NeuroImage, 31:458--467, 2006. [ bib | http ]
[11] L. Song and J. Epps. Classifying EEG for brain-computer interfaces: learning optimal filters for dynamical system features. In International Conference on Machine Learning, 2006. [ bib | .pdf ]
[10] L. Song and J. Epps. Improving the separability of eeg signals during motor imagery with an efficient circular laplacian. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2006. [ bib | .pdf ]
[9] L. Song, E. Gordon, and E. Gysels. Phase synchrony rate for the recognition of motor imagery in bcis. In Advances in Neural Information Processing Systems 18. MIT Press, Cambridge, MA, 2006. [ bib | .pdf ]
[8] L. Song. Desynchronization network analysis for the recognition of imagined movement. In IEEE EMBS Annual International Conference, 2005. [ bib | .pdf ]
[7] W. Huang, C. Murray, X. Shen, L. Song, Y.X. Wu, and L. Zheng. Visualization and analysis of network motifs. In International Conference on Information Visualization, 2005. [ bib | .pdf ]
[6] A. Ahmed, T. Dywer, S. Hong, C. Murray, L. Song, and Y.X. Wu. Visualization and analysis of large and complex scale-free networks. In IEEE VGTC Symposium on Visualization (EUROGRAPHICS), 2005. [ bib | .pdf ]
[5] L. Song and M. Takatsuka. Real-time 3d finger pointing for an augmented desk. In Australasian User Interface Conference, 2005. [ bib | .pdf ]
[4] L. Zheng, L. Song, and P. Eades. Crossing minimization problems of drawing bipartite graphs in two clusters. In Asian-Pacific Symposium on Information Visualization, 2005. [ bib | .pdf ]
[3] S.Q. Liu and L. Song. Curvature relation of wave front and wave changing in external field. Applied Mathematics and Mechanics, 26(7), 2005. [ bib ]
[2] A. Ahmed, T. Dywer, S.H. Hong, C. Murray, L. Song, and Y. Wu. Wilmascope graph visualization. In IEEE Symposium on Information Visualization (InfoVis), 2004. [ bib | .pdf ]
[1] S.Q. Liu and L. Song. The numerical analysis of lobster stomatogastric nervous system. Acta Biophysica Sinica, 20(3), 2004. [ bib ]