Timezone: »
Structured data emerges rapidly in a large number of disciplines: bioinformatics, systems biology, social network analysis, natural language processing and the Internet generate large collections of strings, graphs, trees, and time series. Designing and analysing algorithms for dealing with these large collections of structured data has turned into a major focus of machine learning over recent years, both in the input and output domain of machine learning algorithms, and is starting to enable exciting new applications of machine learning. The goal of this workshop is to bring together experts on learning with structured input and structured output domains and its applications, in order to exchange the latest developments in these growing fields. The workshop will include one session on learning with structured inputs, featuring a keynote by Prof. Eric Xing from Carnegie Mellon University. A second session will focus on learning with structured outputs, with a keynote by Dr. Yasemin Altun from the MPI for Biological Cybernetics. A third session will present novel applications of structured input-structured output learning to real-world problems.
Author Information
Karsten Borgwardt (ETH Zurich)
Karsten Borgwardt is Professor of Data Mining at ETH Zürich, at the Department of Biosystems located in Basel. His work has won several awards, including the NIPS 2009 Outstanding Paper Award, the Krupp Award for Young Professors 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Since 2013, he is heading the Marie Curie Initial Training Network for "Machine Learning for Personalized Medicine" with 12 partner labs in 8 countries (http://www.mlpm.eu). The business magazine "Capital" listed him as one of the "Top 40 under 40" in Science in/from Germany in 2014, 2015 and 2016. For more information, visit: https://www.bsse.ethz.ch/mlcb
Koji Tsuda (University of Tokyo)
Vishwanathan S V N (National ICT Australia)
Xifeng Yan (IBM T. J. Watson Research Center)
More from the Same Authors
-
2020 Poster: Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence »
Bastian Rieck · Tristan Yates · Christian Bock · Karsten Borgwardt · Guy Wolf · Nicholas Turk-Browne · Smita Krishnaswamy -
2020 Spotlight: Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence »
Bastian Rieck · Tristan Yates · Christian Bock · Karsten Borgwardt · Guy Wolf · Nicholas Turk-Browne · Smita Krishnaswamy -
2019 Poster: Wasserstein Weisfeiler-Lehman Graph Kernels »
Matteo Togninalli · Elisabetta Ghisu · Felipe Llinares-Lopez · Bastian Rieck · Karsten Borgwardt -
2019 Spotlight: Wasserstein Weisfeiler-Lehman Graph Kernels »
Matteo Togninalli · Elisabetta Ghisu · Felipe Llinares-López · Bastian Rieck · Karsten Borgwardt -
2016 Poster: Finding significant combinations of features in the presence of categorical covariates »
Laetitia Papaxanthos · Felipe Llinares-López · Dean Bodenham · Karsten Borgwardt -
2015 Poster: Halting in Random Walk Kernels »
Mahito Sugiyama · Karsten Borgwardt -
2013 Poster: Scalable kernels for graphs with continuous attributes »
Aasa Feragen · Niklas Kasenburg · Jens Petersen · Marleen de Bruijne · Karsten Borgwardt -
2013 Poster: Rapid Distance-Based Outlier Detection via Sampling »
Mahito Sugiyama · Karsten Borgwardt -
2013 Poster: It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals »
Barbara Rakitsch · Christoph Lippert · Karsten Borgwardt · Oliver Stegle -
2011 Workshop: From statistical genetics to predictive models in personalized medicine »
Karsten Borgwardt · Oliver Stegle · Shipeng Yu · Glenn Fung · Faisal Farooq · Balaji R Krishnapuram -
2011 Poster: Learning sparse inverse covariance matrices in the presence of confounders »
Oliver Stegle · Christoph Lippert · Joris M Mooij · Neil D Lawrence · Karsten Borgwardt -
2009 Workshop: Transfer Learning for Structured Data »
Sinno Jialin Pan · Ivor W Tsang · Le Song · Karsten Borgwardt · Qiang Yang -
2009 Poster: Fast subtree kernels on graphs »
Nino Shervashidze · Karsten Borgwardt -
2009 Oral: Fast Subtree Kernels on Graphs »
Nino Shervashidze · Karsten Borgwardt -
2009 Poster: Submodularity Cuts and Applications »
Yoshinobu Kawahara · Kiyohito Nagano · Koji Tsuda · Jeffrey A Bilmes -
2009 Spotlight: Submodularity Cuts and Applications »
Yoshinobu Kawahara · Kiyohito Nagano · Koji Tsuda · Jeffrey A Bilmes -
2008 Workshop: Optimization for Machine Learning »
Suvrit Sra · Sebastian Nowozin · Vishwanathan S V N -
2007 Workshop: Machine Learning in Computational Biology (Part 2) »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch · Koji Tsuda -
2007 Workshop: Machine Learning in Computational Biology (Part 1) »
Gal Chechik · Christina Leslie · Quaid Morris · William S Noble · Gunnar Rätsch · Koji Tsuda -
2007 Spotlight: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Oral: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: Colored Maximum Variance Unfolding »
Le Song · Alexander Smola · Karsten Borgwardt · Arthur Gretton -
2007 Poster: Bundle Methods for Machine Learning »
Alexander Smola · Vishwanathan S V N · Quoc V Le -
2007 Demonstration: Elefant »
Kishor Gawande · Alexander Smola · Vishwanathan S V N · Li Cheng · Simon A Guenter -
2006 Workshop: New Problems and Methods in Computational Biology »
Gal Chechik · Quaid Morris · Koji Tsuda · Gunnar Rätsch · Christina Leslie · William S Noble -
2006 Poster: Fast Computation of Graph Kernels »
Vishwanathan S V N · Karsten Borgwardt · Nic Schraudolph -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Fast Iterative Kernel PCA »
Nic Schraudolph · Simon Günter · Vishwanathan S V N -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: implicit Online Learning with Kernels »
Li Cheng · Vishwanathan S V N · Dale Schuurmans · Shaojun Wang · Terry Caelli