Alex J. Smola

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Google Publications

Previous Publications

  •  

    Distributed Large-scale Natural Graph Factorization

    Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy,, Vanja Josifovski, Alexander J Smola

    Proceedings of the 22nd International World Wide Web Conference (WWW 2013) (to appear)

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    A Kernel Two-Sample Test

    Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

    Journal of Machine Learning Research, vol. 13 (2012), pp. 723-773

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    Discovering Geographical Topics In The Twitter Stream

    Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Kostas Tsioutsiouliklis, Alex Smola

    Proceedings of The 21st International World Wide Web conference (WWW) (2012)

  •  

    Exponential Families for Conditional Random Fields

    Yasemin Altun, Alexander J. Smola, Thomas Hofmann

    CoRR, vol. abs/1207.4131 (2012)

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    Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations

    Nando de Freitas, Alex J. Smola, Masrour Zoghi

    ICML (2012)

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    Fair and Balanced: Learning to Present News Stories

    Amr Ahmed, Choon-hui Teo, S.V.N Vishwanathan, Alex Smola

    Proceedings of The 5th ACM International Conference on Web Search and Data Mining (WSDM) (2012)

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    Friend or frenemy?: predicting signed ties in social networks

    Shuang-Hong Yang, Alexander J. Smola, Bo Long, Hongyuan Zha, Yi Chang

    SIGIR (2012), pp. 555-564

  •  

    Hokusai - Sketching Streams in Real Time

    Sergiy Matusevych, Alex J. Smola, Amr Ahmed

    CoRR, vol. abs/1210.4891 (2012)

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    Learning Networks of Heterogeneous Influence

    Nan Du, Le Song, Alex J. Smola, Ming Yuan

    NIPS (2012), pp. 2789-2797

  •  

    Linear support vector machines via dual cached loops

    Shin Matsushima, S. V. N. Vishwanathan, Alexander J. Smola

    KDD (2012), pp. 177-185

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    Regret Bounds for Deterministic Gaussian Process Bandits

    Nando de Freitas, Alex J. Smola, Masrour Zoghi

    CoRR, vol. abs/1203.2177 (2012)

  •   

    Scalable Inference in Latent Variable Models

    Amr Ahmed, Mohamed Aly, Joseph Gonzalez, Shravan Narayanamurthy, Alex Smola

    Proceedings of The 5th ACM International Conference on Web Search and Data Mining (WSDM) (2012)

  •  

    Super-Samples from Kernel Herding

    Yutian Chen, Max Welling, Alex J. Smola

    CoRR, vol. abs/1203.3472 (2012)

  •   

    Tutorial: New Templates for Scalable Data Analysis

    Amr Ahmed, Alex Smola, Markus Weimer

    The 21st International World Wide Web conference (WWW) (2012)

  •  

    Bid generation for advanced match in sponsored search

    Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, Alex J. Smola

    WSDM (2011), pp. 515-524

  •  

    Collaborative competitive filtering: learning recommender using context of user choice

    Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, Zhaohui Zheng

    SIGIR (2011), pp. 295-304

  •  

    Guest editorial: model selection and optimization in machine learning

    Süreyya Özögür-Akyüz, Devrim Ünay, Alexander J. Smola

    Machine Learning, vol. 85 (2011), pp. 1-2

  •  

    Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models

    Qinfeng Shi, Li Cheng, Li Wang, Alex J. Smola

    International Journal of Computer Vision, vol. 93 (2011), pp. 22-32

  •  

    Like like alike: joint friendship and interest propagation in social networks

    Shuang-Hong Yang, Bo Long, Alexander J. Smola, Narayanan Sadagopan, Zhaohui Zheng, Hongyuan Zha

    WWW (2011), pp. 537-546

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    Linear-Time Estimators for Propensity Scores

    Deepak Agarwal, Lihong Li, Alexander J. Smola

    Journal of Machine Learning Research - Proceedings Track, vol. 15 (2011), pp. 93-100

  •  

    Multiple domain user personalization

    Yucheng Low, Deepak Agarwal, Alexander J. Smola

    KDD (2011), pp. 123-131

  •   

    Online Inference for the Infinite Cluster-topic Model: Storylines from Streaming Text

    Amr Ahmed, Qirong Ho, Choon-hui Teo, Jacobe Eisenstein, Alex Smola, Eric Xing

    Proceedings of the 14th Conference on Artificial Intelligence and Statistics (AISTATS) (2011)

  •  

    Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text

    Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alexander J. Smola, Eric P. Xing

    Journal of Machine Learning Research - Proceedings Track, vol. 15 (2011), pp. 101-109

  •  

    Parallel Online Learning

    Daniel Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola

    CoRR, vol. abs/1103.4204 (2011)

  •  

    Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting

    Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, Alex Smola

    Proceedings of The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2011., ACM

  •  

    Scalable clustering of news search results

    Srinivas Vadrevu, Choon Hui Teo, Suju Rajan, Kunal Punera, Byron Dom, Alexander J. Smola, Yi Chang, Zhaohui Zheng

    WSDM (2011), pp. 675-684

  •   

    Tutorial: Latent Variable Models for the Internet

    Amr Ahmed, Alex Smola

    the 20th International World Wide Web conference (WWW) (2011)

  •   

    Tutorial: Graphical Models for the Internet

    Amr Ahmed, Alex Smola

    The 25th Conference on Neural Information Processing Systems. (NIPS), (2011)

  •  

    Unified analysis of streaming news

    Amr Ahmed, Qirong Ho, Jacob Eisenstein, Eric P. Xing, Alexander J. Smola, Choon Hui Teo

    WWW (2011), pp. 267-276

  •   

    Unified Analysis of Streaming News

    Amr Ahmed, Qirong Ho, Jacob Eisenstein, Eric P. Xing, Alex Smola, Choon-hui Teo

    Proceedings of the 20th International World Wide Web conference (WWW) (2011)

  •  

    WWW 2011 invited tutorial overview: latent variable models on the internet

    Amr Ahmed, Alexander J. Smola

    WWW (Companion Volume) (2011), pp. 281-282

  •  

    An Architecture for Parallel Topic Models

    Alexander J. Smola, Shravan M. Narayanamurthy

    PVLDB, vol. 3 (2010), pp. 703-710

  •  

    Bundle Methods for Regularized Risk Minimization

    Choon Hui Teo, S. V. N. Vishwanathan, Alex J. Smola, Quoc V. Le

    Journal of Machine Learning Research, vol. 11 (2010), pp. 311-365

  •  

    Collaborative Filtering on a Budget

    Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer

    Journal of Machine Learning Research - Proceedings Track, vol. 9 (2010), pp. 389-396

  •  

    Discriminative frequent subgraph mining with optimality guarantees

    Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt

    Statistical Analysis and Data Mining, vol. 3 (2010), pp. 302-318

  •  

    Distributed Flow Algorithms for Scalable Similarity Visualization

    Novi Quadrianto, Dale Schuurmans, Alex J. Smola

    ICDM Workshops (2010), pp. 1220-1227

  •   

    Hilbert Space Embeddings of Hidden Markov Models

    Le Song, Byron Boots, Sajid Siddiqi, Geoffrey J. Gordon, Alex Smola

    Proceedings of the International Conference on Machine Learning (ICML) (2010)

  •  

    IntervalRank: isotonic regression with listwise and pairwise constraints

    Taesup Moon, Alex J. Smola, Yi Chang, Zhaohui Zheng

    WSDM (2010), pp. 151-160

  •  

    Kernelized Sorting

    Novi Quadrianto, Alex J. Smola, Le Song, Tinne Tuytelaars

    IEEE Trans. Pattern Anal. Mach. Intell., vol. 32 (2010), pp. 1809-1821

  •  

    Multitask Learning without Label Correspondences

    Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, S. V. N. Vishwanathan, James Petterson

    NIPS (2010), pp. 1957-1965

  •  

    Optimal Web-Scale Tiering as a Flow Problem

    Gilbert Leung, Novi Quadrianto, Alexander J. Smola, Kostas Tsioutsiouliklis

    NIPS (2010), pp. 1333-1341

  •  

    Parallelized Stochastic Gradient Descent

    Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li

    NIPS (2010), pp. 2595-2603

  •  

    Super-Samples from Kernel Herding

    Yutian Chen, Max Welling, Alex J. Smola

    UAI (2010), pp. 109-116

  •  

    Wearable sensor activity analysis using semi-Markov models with a grammar

    O. Thomas, Peter Sunehag, Gideon Dror, S. Yun, S. Kim, Matthew W. Robards, Alexander J. Smola, D. Green, P. Saunders

    Pervasive and Mobile Computing, vol. 6 (2010), pp. 342-350

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    Word Features for Latent Dirichlet Allocation

    James Petterson, Alexander J. Smola, Tibério S. Caetano, Wray L. Buntine, Shravan M. Narayanamurthy

    NIPS (2010), pp. 1921-1929

  •  

    Distribution Matching for Transduction

    Novi Quadrianto, James Petterson, Alex J. Smola

    NIPS (2009), pp. 1500-1508

  •  

    Estimating Labels from Label Proportions

    Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le

    Journal of Machine Learning Research, vol. 10 (2009), pp. 2349-2374

  •  

    Feature Hashing for Large Scale Multitask Learning

    Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alex J. Smola

    CoRR, vol. abs/0902.2206 (2009)

  •  

    Hash Kernels

    Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan

    Journal of Machine Learning Research - Proceedings Track, vol. 5 (2009), pp. 496-503

  •  

    Hash Kernels for Structured Data

    Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan

    Journal of Machine Learning Research, vol. 10 (2009), pp. 2615-2637

  •  

    Hilbert space embeddings of conditional distributions with applications to dynamical systems

    Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu

    ICML (2009), pp. 121

  •  

    Near-optimal Supervised Feature Selection among Frequent Subgraphs

    Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt

    SDM (2009), pp. 1075-1086

  •  

    Relative Novelty Detection

    Alexander J. Smola, Le Song, Choon Hui Teo

    Journal of Machine Learning Research - Proceedings Track, vol. 5 (2009), pp. 536-543

  •  

    Slow Learners are Fast

    Martin Zinkevich, Alex J. Smola, John Langford

    NIPS (2009), pp. 2331-2339

  •  

    A Kernel Method for the Two-Sample Problem

    Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

    CoRR, vol. abs/0805.2368 (2008)

  •  

    Adaptive collaborative filtering

    Markus Weimer, Alexandros Karatzoglou, Alex J. Smola

    RecSys (2008), pp. 275-282

  •  

    Discriminative human action segmentation and recognition using semi-Markov model

    Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola

    CVPR (2008)

  •  

    Estimating labels from label proportions

    Novi Quadrianto, Alex J. Smola, Tibério S. Caetano, Quoc V. Le

    ICML (2008), pp. 776-783

  •  

    Improving Maximum Margin Matrix Factorization

    Markus Weimer, Alexandros Karatzoglou, Alex J. Smola

    ECML/PKDD (1) (2008), pp. 14

  •  

    Improving maximum margin matrix factorization

    Markus Weimer, Alexandros Karatzoglou, Alex J. Smola

    Machine Learning, vol. 72 (2008), pp. 263-276

  •  

    Kernel Measures of Independence for non-iid Data

    Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola

    NIPS (2008), pp. 1937-1944

  •  

    Kernelized Sorting

    Novi Quadrianto, Le Song, Alex J. Smola

    NIPS (2008), pp. 1289-1296

  •  

    Learning Graph Matching

    Tibério S. Caetano, Julian John McAuley, Li Cheng, Quoc V. Le, Alex J. Smola

    CoRR, vol. abs/0806.2890 (2008)

  •  

    Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

    Ahmed H. El Zein, Eric McCreath, Alistair P. Rendell, Alex J. Smola

    ICCS (1) (2008), pp. 466-475

  •  

    Robust Near-Isometric Matching via Structured Learning of Graphical Models

    Julian John McAuley, Tibério S. Caetano, Alexander J. Smola

    NIPS (2008), pp. 1057-1064

  •  

    Robust Near-Isometric Matching via Structured Learning of Graphical Models

    Julian John McAuley, Tibério S. Caetano, Alexander J. Smola

    CoRR, vol. abs/0809.3618 (2008)

  •  

    Tailoring density estimation via reproducing kernel moment matching

    Le Song, Xinhua Zhang, Alex J. Smola, Arthur Gretton, Bernhard Schölkopf

    ICML (2008), pp. 992-999

  •  

    Tighter Bounds for Structured Estimation

    Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo

    NIPS (2008), pp. 281-288

  •  

    A Hilbert Space Embedding for Distributions

    Alex J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf

    ALT (2007), pp. 13-31

  •  

    A Kernel Approach to Comparing Distributions

    Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

    AAAI (2007), pp. 1637-1641

  •  

    A Kernel Statistical Test of Independence

    Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex J. Smola

    NIPS (2007)

  •  

    A dependence maximization view of clustering

    Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt

    ICML (2007), pp. 815-822

  •  

    A scalable modular convex solver for regularized risk minimization

    Choon Hui Teo, Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le

    KDD (2007), pp. 727-736

  •  

    Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes

    S. V. N. Vishwanathan, Alexander J. Smola, René Vidal

    International Journal of Computer Vision, vol. 73 (2007), pp. 95-119

  •  

    Bundle Methods for Machine Learning

    Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le

    NIPS (2007)

  •  

    COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking

    Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alex J. Smola

    NIPS (2007)

  •  

    Colored Maximum Variance Unfolding

    Le Song, Alex J. Smola, Karsten M. Borgwardt, Arthur Gretton

    NIPS (2007)

  •  

    Convex Learning with Invariances

    Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola

    NIPS (2007)

  •  

    Direct Optimization of Ranking Measures

    Quoc V. Le, Alexander J. Smola

    CoRR, vol. abs/0704.3359 (2007)

  •  

    Gene selection via the BAHSIC family of algorithms

    Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola

    ISMB/ECCB (Supplement of Bioinformatics) (2007), pp. 490-498

  •  

    Learning Graph Matching

    Tibério S. Caetano, Li Cheng, Quoc V. Le, Alex J. Smola

    ICCV (2007), pp. 1-8

  •  

    Semi-Markov Models for Sequence Segmentation

    Qinfeng Shi, Yasemin Altun, Alex J. Smola, S. V. N. Vishwanathan

    EMNLP-CoNLL (2007), pp. 640-648

  •  

    Supervised feature selection via dependence estimation

    Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo

    ICML (2007), pp. 823-830

  •   

    The Need for Open Source Software in Machine Learning

    Soren Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoff Holmes, Yann LeCun, Klaus-Robert Mueller, Fernando Pereira, Carl-Edward Rasmussen, Gunnar Raetsch, Bernhard Schoelkopf, Alexander Smola, Pascal Vincent, Jason Weston, Robert C. Williamson

    Journal of Machine Learning Research, vol. 8 (2007), pp. 2443-2466

  •  

    A Kernel Method for the Two-Sample-Problem

    Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

    NIPS (2006), pp. 513-520

  •  

    Correcting Sample Selection Bias by Unlabeled Data

    Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf

    NIPS (2006), pp. 601-608

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    Integrating structured biological data by Kernel Maximum Mean Discrepancy

    Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola

    ISMB (Supplement of Bioinformatics) (2006), pp. 49-57

  •  

    Kernel extrapolation

    S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola

    Neurocomputing, vol. 69 (2006), pp. 721-729

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    Kernel methods and the exponential family

    Stéphane Canu, Alexander J. Smola

    Neurocomputing, vol. 69 (2006), pp. 714-720

  •  

    Learning high-order MRF priors of color images

    Julian John McAuley, Tibério S. Caetano, Alex J. Smola, Matthias O. Franz

    ICML (2006), pp. 617-624

  •  

    Newton-Like Methods for Nonparametric Independent Component Analysis

    Hao Shen, Knut Hüper, Alexander J. Smola

    ICONIP (1) (2006), pp. 1068-1077

  •  

    Nonparametric Quantile Estimation

    Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola

    Journal of Machine Learning Research, vol. 7 (2006), pp. 1231-1264

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    Second Order Cone Programming Approaches for Handling Missing and Uncertain Data

    Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola

    Journal of Machine Learning Research, vol. 7 (2006), pp. 1283-1314

  •  

    Simpler knowledge-based support vector machines

    Quoc V. Le, Alex J. Smola, Thomas Gärtner

    ICML (2006), pp. 521-528

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    Step Size Adaptation in Reproducing Kernel Hilbert Space

    S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex J. Smola

    Journal of Machine Learning Research, vol. 7 (2006), pp. 1107-1133

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    Transductive Gaussian Process Regression with Automatic Model Selection

    Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun

    ECML (2006), pp. 306-317

  •  

    Unifying Divergence Minimization and Statistical Inference Via Convex Duality

    Yasemin Altun, Alexander J. Smola

    COLT (2006), pp. 139-153

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    Boîte à outils SVM simple et rapide

    Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay

    Revue d'Intelligence Artificielle, vol. 19 (2005), pp. 741-767

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    Experimentally optimal nu in support vector regression for different noise models and parameter settings

    Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola

    Neural Networks, vol. 18 (2005), pp. 205-

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    Heteroscedastic Gaussian process regression

    Quoc V. Le, Alexander J. Smola, Stéphane Canu

    ICML (2005), pp. 489-496

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    Joint Regularization

    Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola

    ESANN (2005), pp. 455-460

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    Kernel Methods for Measuring Independence

    Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf

    Journal of Machine Learning Research, vol. 6 (2005), pp. 2075-2129

  •  

    Kernel methods and the exponential family

    Stéphane Canu, Alexander J. Smola

    ESANN (2005), pp. 447-454

  •  

    Large-Scale Multiclass Transduction

    Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, S. V. N. Vishwanathan

    NIPS (2005)

  •  

    Learning the Kernel with Hyperkernels

    Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson

    Journal of Machine Learning Research, vol. 6 (2005), pp. 1043-1071

  •  

    Measuring Statistical Dependence with Hilbert-Schmidt Norms

    Arthur Gretton, Olivier Bousquet, Alex J. Smola, Bernhard Schölkopf

    ALT (2005), pp. 63-77

  •  

    Protein function prediction via graph kernels

    Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel

    ISMB (Supplement of Bioinformatics) (2005), pp. 47-56

  •  

    Universal Clustering with Regularization in Probabilistic Space

    Vladimir Nikulin, Alex J. Smola

    MLDM (2005), pp. 142-152

  •  

    A Second Order Cone programming Formulation for Classifying Missing Data

    Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola

    NIPS (2004)

  •  

    A tutorial on support vector regression

    Alexander J. Smola, Bernhard Schölkopf

    Statistics and Computing, vol. 14 (2004), pp. 199-222

  •  

    Binet-Cauchy Kernels

    S. V. N. Vishwanathan, Alex J. Smola

    NIPS (2004)

  •  

    Experimentally optimal v in support vector regression for different noise models and parameter settings

    Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola

    Neural Networks, vol. 17 (2004), pp. 127-141

  •  

    Exponential Families for Conditional Random Fields

    Yasemin Altun, Alexander J. Smola, Thomas Hofmann

    UAI (2004), pp. 2-9

  •   

    Gaussian process classification for segmenting and annotating sequences

    Yasemin Altun, Thomas Hofmann, Alex J. Smola

    ICML (2004)

  •  

    Learning with non-positive kernels

    Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola

    ICML (2004)

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    Classification in a normalized feature space using support vector machines

    Arnulf B. A. Graf, Alexander J. Smola, Silvio Borer

    IEEE Transactions on Neural Networks, vol. 14 (2003), pp. 597-605

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    Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

    Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, A.J. Smola, Klaus-Robert Müller

    IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25 (2003), pp. 623-628

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    Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

    Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller

    IEEE Trans. Pattern Anal. Mach. Intell., vol. 25 (2003), pp. 623-633

  •  

    Kernels and Regularization on Graphs

    Alex J. Smola, Risi Imre Kondor

    COLT (2003), pp. 144-158

  •  

    Laplace Propagation

    Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin

    NIPS (2003)

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    Machine Learning with Hyperkernels

    Cheng Soon Ong, Alex J. Smola

    ICML (2003), pp. 568-575

  •  

    SimpleSVM

    S. V. N. Vishwanathan, Alex J. Smola, M. Narasimha Murty

    ICML (2003), pp. 760-767

  •  

    A Short Introduction to Learning with Kernels

    Bernhard Schölkopf, Alex J. Smola

    Machine Learning Summer School (2002), pp. 41-64

  •  

    Adapting Codes and Embeddings for Polychotomies

    Gunnar Rätsch, Alexander J. Smola, Sebastian Mika

    NIPS (2002), pp. 513-520

  •  

    Bayesian Kernel Methods

    Alex J. Smola, Bernhard Schölkopf

    Machine Learning Summer School (2002), pp. 65-117

  •  

    Fast Kernels for String and Tree Matching

    S. V. N. Vishwanathan, Alexander J. Smola

    NIPS (2002), pp. 569-576

  •  

    Hyperkernels

    Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson

    NIPS (2002), pp. 478-485

  •  

    Large Margin Classification for Moving Targets

    Jyrki Kivinen, Alex J. Smola, Robert C. Williamson

    ALT (2002), pp. 113-127

  •  

    Minimal Kernel Classifiers

    Glenn Fung, Olvi L. Mangasarian, Alex J. Smola

    Journal of Machine Learning Research, vol. 3 (2002), pp. 303-321

  •  

    Multi-Instance Kernels

    Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola

    ICML (2002), pp. 179-186

  •  

    A Generalized Representer Theorem

    Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola

    COLT/EuroCOLT (2001), pp. 416-426

  •  

    Estimating the Support of a High-Dimensional Distribution

    Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alex J. Smola, Robert C. Williamson

    Neural Computation, vol. 13 (2001), pp. 1443-1471

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    Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

    Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf

    IEEE Transactions on Information Theory, vol. 47 (2001), pp. 2516-2532

  •  

    Kernel Machines and Boolean Functions

    Adam Kowalczyk, Alex J. Smola, Robert C. Williamson

    NIPS (2001), pp. 439-446

  •  

    Online Learning with Kernels

    Jyrki Kivinen, Alex J. Smola, Robert C. Williamson

    NIPS (2001), pp. 785-792

  •  

    Regularized Principal Manifolds

    Alex J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson

    Journal of Machine Learning Research, vol. 1 (2001), pp. 179-209

  •  

    Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments

    Athanassia Chalimourda, Bernhard Schölkopf, Alex J. Smola

    IJCNN (5) (2000), pp. 199-204

  •  

    Entropy Numbers of Linear Function Classes

    Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf

    COLT (2000), pp. 309-319

  •  

    New Support Vector Algorithms

    Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett

    Neural Computation, vol. 12 (2000), pp. 1207-1245

  •  

    Query Learning with Large Margin Classifiers

    Colin Campbell, Nello Cristianini, Alex J. Smola

    ICML (2000), pp. 111-118

  •  

    Regularization with Dot-Product Kernels

    Alex J. Smola, Zoltán L. Óvári, Robert C. Williamson

    NIPS (2000), pp. 308-314

  •  

    Robust Ensemble Learning for Data Mining

    Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller

    PAKDD (2000), pp. 341-344

  •  

    Sparse Greedy Gaussian Process Regression

    Alex J. Smola, Peter L. Bartlett

    NIPS (2000), pp. 619-625

  •  

    Sparse Greedy Matrix Approximation for Machine Learning

    Alex J. Smola, Bernhard Schölkopf

    ICML (2000), pp. 911-918

  •  

    Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

    Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alex J. Smola, Thomas Lengauer, Klaus-Robert Müller

    German Conference on Bioinformatics (1999), pp. 37-43

  •  

    Entropy Numbers, Operators and Support Vector Kernels

    Robert C. Williamson, Alex J. Smola, Bernhard Schölkopf

    EuroCOLT (1999), pp. 285-299

  •  

    Input space versus feature space in kernel-based methods

    Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola

    IEEE Transactions on Neural Networks, vol. 10 (1999), pp. 1000-1017

  •  

    Invariant Feature Extraction and Classification in Kernel Spaces

    Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller

    NIPS (1999), pp. 526-532

  •  

    Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten

    Bernhard Schölkopf, Klaus-Robert Müller, Alex J. Smola

    Inform., Forsch. Entwickl., vol. 14 (1999), pp. 154-163

  •  

    Regularized Principal Manifolds

    Alex J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf

    EuroCOLT (1999), pp. 214-229

  •  

    Support Vector Method for Novelty Detection

    Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt

    NIPS (1999), pp. 582-588

  •  

    The Entropy Regularization Information Criterion

    Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson

    NIPS (1999), pp. 342-348

  •  

    v-Arc: Ensemble Learning in the Presence of Outliers

    Gunnar Rätsch, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika

    NIPS (1999), pp. 561-567

  •  

    Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces

    Bernhard Schölkopf, Alex J. Smola, Phil Knirsch, Chris Burges

    DAGM-Symposium (1998), pp. 125-132

  •  

    Kernel PCA and De-Noising in Feature Spaces

    Sebastian Mika, Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch

    NIPS (1998), pp. 536-542

  •  

    Nonlinear Component Analysis as a Kernel Eigenvalue Problem

    Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller

    Neural Computation, vol. 10 (1998), pp. 1299-1319

  •  

    On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

    Alex J. Smola, Bernhard Schölkopf

    Algorithmica, vol. 22 (1998), pp. 211-231

  •  

    Semiparametric Support Vector and Linear Programming Machines

    Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf

    NIPS (1998), pp. 585-591

  •  

    Shrinking the Tube: A New Support Vector Regression Algorithm

    Bernhard Schölkopf, Peter L. Bartlett, Alex J. Smola, Robert C. Williamson

    NIPS (1998), pp. 330-336

  •  

    The connection between regularization operators and support vector kernels

    Alex J. Smola, Bernhard Schölkopf, Klaus-Robert Müller

    Neural Networks, vol. 11 (1998), pp. 637-649

  •  

    From Regularization Operators to Support Vector Kernels

    Alex J. Smola, Bernhard Schölkopf

    NIPS (1997)

  •  

    Kernel Principal Component Analysis

    Bernhard Schölkopf, Alex J. Smola, Klaus-Robert Müller

    ICANN (1997), pp. 583-588

  •  

    Predicting Time Series with Support Vector Machines

    Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik

    ICANN (1997), pp. 999-1004

  •  

    Prior Knowledge in Support Vector Kernels

    Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik

    NIPS (1997)

  •  

    Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

    Vladimir Vapnik, Steven E. Golowich, Alex J. Smola

    NIPS (1996), pp. 281-287

  •  

    Support Vector Regression Machines

    Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik

    NIPS (1996), pp. 155-161