| Sphereface: Deep hypersphere embedding for face recognition W Liu, Y Wen, Z Yu, M Li, B Raj, L Song Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1220 | 2017 |
| A Hilbert space embedding for distributions A Smola, A Gretton, L Song, B Schölkopf International Conference on Algorithmic Learning Theory, 13-31, 2007 | 669 | 2007 |
| A kernel statistical test of independence A Gretton, K Fukumizu, CH Teo, L Song, B Schölkopf, AJ Smola Advances in neural information processing systems, 585-592, 2008 | 545 | 2008 |
| Learning combinatorial optimization algorithms over graphs E Khalil, H Dai, Y Zhang, B Dilkina, L Song Advances in neural information processing systems, 6348-6358, 2017 | 439 | 2017 |
| Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International conference on machine learning, 2702-2711, 2016 | 341 | 2016 |
| Feature selection via dependence maximization L Song, A Smola, A Gretton, J Bedo, K Borgwardt The Journal of Machine Learning Research 13 (1), 1393-1434, 2012 | 317 | 2012 |
| Recurrent marked temporal point processes: Embedding event history to vector N Du, H Dai, R Trivedi, U Upadhyay, M Gomez-Rodriguez, L Song Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 313 | 2016 |
| Supervised feature selection via dependence estimation L Song, A Smola, A Gretton, KM Borgwardt, J Bedo Proceedings of the 24th international conference on Machine learning, 823-830, 2007 | 302 | 2007 |
| Estimating time-varying networks M Kolar, L Song, A Ahmed, EP Xing The Annals of Applied Statistics 4 (1), 94-123, 2010 | 288 | 2010 |
| Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes K Zhou, H Zha, L Song Artificial Intelligence and Statistics, 641-649, 2013 | 282 | 2013 |
| The ‘when’and ‘where’of perceiving signals of threat versus non-threat LM Williams, D Palmer, BJ Liddell, L Song, E Gordon Neuroimage 31 (1), 458-467, 2006 | 274 | 2006 |
| Scalable influence estimation in continuous-time diffusion networks N Du, L Song, M Gomez Rodriguez, H Zha Advances in neural information processing systems 26, 3147-3155, 2013 | 271 | 2013 |
| GRAM: graph-based attention model for healthcare representation learning E Choi, MT Bahadori, L Song, WF Stewart, J Sun Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 266 | 2017 |
| Deep fried convnets Z Yang, M Moczulski, M Denil, N de Freitas, A Smola, L Song, Z Wang Proceedings of the IEEE International Conference on Computer Vision, 1476-1483, 2015 | 260 | 2015 |
| Influence estimation and maximization in continuous-time diffusion networks M Gomez-Rodriguez, L Song, N Du, H Zha, B Schölkopf ACM Transactions on Information Systems (TOIS) 34 (2), 1-33, 2016 | 238 | 2016 |
| Hilbert space embeddings of conditional distributions with applications to dynamical systems L Song, J Huang, A Smola, K Fukumizu Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 232 | 2009 |
| Hilbert space embeddings of hidden Markov models L Song, B Boots, S Siddiqi, GJ Gordon, A Smola | 222 | 2010 |
| A state-space mixed membership blockmodel for dynamic network tomography EP Xing, W Fu, L Song The Annals of Applied Statistics 4 (2), 535-566, 2010 | 215 | 2010 |
| Time-varying dynamic bayesian networks L Song, M Kolar, E Xing Advances in neural information processing systems 22, 1732-1740, 2009 | 202 | 2009 |
| Neural network-based graph embedding for cross-platform binary code similarity detection X Xu, C Liu, Q Feng, H Yin, L Song, D Song Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, 2017 | 196 | 2017 |
Alex SmolaAmazon Web ServicesVerified email at smola.org
