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Alexander J. Smola: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Yasemin Altun, Alexander J. Smola
    Unifying Divergence Minimization and Statistical Inference Via Convex Duality. [Citation Graph (0, 0)][DBLP]
    COLT, 2006, pp:139-153 [Conf]
  2. Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun
    Transductive Gaussian Process Regression with Automatic Model Selection. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:306-317 [Conf]
  3. Stéphane Canu, Alexander J. Smola
    Kernel methods and the exponential family. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:447-454 [Conf]
  4. Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola
    Joint Regularization. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:455-460 [Conf]
  5. Quoc V. Le, Alexander J. Smola, Stéphane Canu
    Heteroscedastic Gaussian process regression. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:489-496 [Conf]
  6. Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola
    Learning with non-positive kernels. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  7. Hao Shen, Knut Hüper, Alexander J. Smola
    Newton-Like Methods for Nonparametric Independent Component Analysis. [Citation Graph (0, 0)][DBLP]
    ICONIP (1), 2006, pp:1068-1077 [Conf]
  8. Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola
    Integrating structured biological data by Kernel Maximum Mean Discrepancy. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:49-57 [Conf]
  9. Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel
    Protein function prediction via graph kernels. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2005, pp:47-56 [Conf]
  10. Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson
    Hyperkernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:478-485 [Conf]
  11. Gunnar Rätsch, Alexander J. Smola, Sebastian Mika
    Adapting Codes and Embeddings for Polychotomies. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:513-520 [Conf]
  12. Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin
    Laplace Propagation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  13. S. V. N. Vishwanathan, Alexander J. Smola
    Fast Kernels for String and Tree Matching. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:569-576 [Conf]
  14. S. V. N. Vishwanathan, Alexander J. Smola, René Vidal
    Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. [Citation Graph (0, 0)][DBLP]
    International Journal of Computer Vision, 2007, v:73, n:1, pp:95-119 [Journal]
  15. Stéphane Canu, Alexander J. Smola
    Kernel methods and the exponential family. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:7-9, pp:714-720 [Journal]
  16. S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola
    Kernel extrapolation. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:7-9, pp:721-729 [Journal]
  17. Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf
    Kernel Methods for Measuring Independence. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:2075-2129 [Journal]
  18. Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson
    Learning the Kernel with Hyperkernels. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:1043-1071 [Journal]
  19. Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola
    Nonparametric Quantile Estimation. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1231-1264 [Journal]
  20. Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola
    Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1283-1314 [Journal]
  21. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
    A Kernel Approach to Comparing Distributions. [Citation Graph (0, 0)][DBLP]
    AAAI, 2007, pp:1637-1641 [Conf]
  22. Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
    A Hilbert Space Embedding for Distributions. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2007, pp:40-41 [Conf]
  23. Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt
    A dependence maximization view of clustering. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:815-822 [Conf]
  24. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
    A Kernel Method for the Two-Sample-Problem. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:513-520 [Conf]
  25. Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf
    Correcting Sample Selection Bias by Unlabeled Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:601-608 [Conf]
  26. Yasemin Altun, Alexander J. Smola, Thomas Hofmann
    Exponential Families for Conditional Random Fields. [Citation Graph (0, 0)][DBLP]
    UAI, 2004, pp:2-9 [Conf]
  27. Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo
    Supervised Feature Selection via Dependence Estimation [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  28. Quoc Le, Alexander J. Smola
    Direct Optimization of Ranking Measures [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]

  29. Discriminative human action segmentation and recognition using semi-Markov model. [Citation Graph (, )][DBLP]


  30. Hilbert space embeddings of conditional distributions with applications to dynamical systems. [Citation Graph (, )][DBLP]


  31. Feature hashing for large scale multitask learning. [Citation Graph (, )][DBLP]


  32. Hilbert Space Embeddings of Hidden Markov Models. [Citation Graph (, )][DBLP]


  33. Gene selection via the BAHSIC family of algorithms. [Citation Graph (, )][DBLP]


  34. Tighter Bounds for Structured Estimation. [Citation Graph (, )][DBLP]


  35. Robust Near-Isometric Matching via Structured Learning of Graphical Models. [Citation Graph (, )][DBLP]


  36. Near-optimal Supervised Feature Selection among Frequent Subgraphs. [Citation Graph (, )][DBLP]


  37. A Kernel Method for the Two-Sample Problem [Citation Graph (, )][DBLP]


  38. Robust Near-Isometric Matching via Structured Learning of Graphical Models [Citation Graph (, )][DBLP]


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