Shankar Vembu

According to our database, Shankar Vembu authored at least 29 papers between 2004 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2021

A qualitative study of large-scale recommendation algorithms for biomedical knowledge bases.

Ehsan Noei, Tsahi Hayat, Jessica Perrie, Recep Colak, Yanqi Hao, Shankar Vembu, Kelly A. Lyons, Sam Molyneux

Int. J. Digit. Libr., 2021

2020

The evolutionary history of 2,658 cancers.

Moritz Gerstung, Clemency Jolly, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Kaixian Yu, Maxime Tarabichi, Amit G. Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vázquez-García, Kerstin Haase, Lara Jerman, Subhajit Sengupta, Geoff MacIntyre, Salem Malikic, Nilgun Donmez, Dimitri G. Livitz, Marek Cmero, Jonas Demeulemeester, Steven E. Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C. Boutros, David D. L. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, Süleyman Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Yuan, Wenyi Wang, Quaid D. Morris, David J. Adams, Peter J. Campbell, Shaolong Cao, Elizabeth L. Christie, Yupeng Cun, Kevin J. Dawson, Ruben M. Drews, Roland Eils, Matthew Fittall, Dale W. Garsed, Gavin Ha, Henry Lee-Six, Iñigo Martincorena, Layla Oesper, Myron Peto, Benjamin J. Raphael, Daniel T. Rosebrock, Adriana Salcedo, Ruian Shi, Seung Jun Shin, Oliver Spiro, Lincoln D. Stein, Shankar Vembu, David A. Wheeler, Tsun-Po Yang, Paul T. Spellman, David C. Wedge, Peter Van Loo

Nat., 2020

Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.

Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, Andrew McCallum

Mach. Learn., 2020

2019

Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition.

Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, Andrew McCallum

CoRR, 2019

2017

Implementing Recommendation Algorithms in a Large-Scale Biomedical Science Knowledge Base.

Jessica Perrie, Yanqi Hao, Zack Hayat, Recep Colak, Kelly A. Lyons, Shankar Vembu, Sam Molyneux

CoRR, 2017

2016

Interactive Learning from Multiple Noisy Labels.

Shankar Vembu, Sandra Zilles

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

2015

Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors.

Amit G. Deshwar, Shankar Vembu, Quaid Morris

Proceedings of the Biocomputing 2015: Proceedings of the Pacific Symposium, 2015

2014

Reconstructing subclonal composition and evolution from whole genome sequencing of tumors.

Amit G. Deshwar, Shankar Vembu, Christina K. Yung, Gun Ho Jang, Lincoln Stein, Quaid Morris

CoRR, 2014

Inferring clonal evolution of tumors from single nucleotide somatic mutations.

Wei Jiao, Shankar Vembu, Amit G. Deshwar, Lincoln Stein, Quaid Morris

BMC Bioinform., 2014

An Efficient Algorithm to Integrate Network and Attribute Data for Gene Function Prediction.

Shankar Vembu, Quaid Morris

Proceedings of the Biocomputing 2014: Proceedings of the Pacific Symposium, 2014

2013

Beam search algorithms for multilabel learning.

Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan

Mach. Learn., 2013

2012

Inhibition in Multiclass Classification.

Ramón Huerta, Shankar Vembu, José M. Amigó, Thomas Nowotny, Charles Elkan

Neural Comput., 2012

Modeling the Clonal Evolution of Cancer from Next Generation Sequencing Data

Wei Jiao, Shankar Vembu, Amit G. Deshwar, Lincoln Stein, Quaid Morris

CoRR, 2012

Learning and Inference in Probabilistic Classifier Chains with Beam Search.

Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Predicting accurate probabilities with a ranking loss.

Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado

Proceedings of the 29th International Conference on Machine Learning, 2012

Dynamical SVM for Time Series Classification.

Ramón Huerta, Shankar Vembu, Mehmet K. Muezzinoglu, Alexander Vergara

Proceedings of the Pattern Recognition, 2012

2010

Learning to predict combinatorial structures.

Shankar Vembu

PhD thesis, 2010

Automatic Model Adaptation for Complex Structured Domains.

Geoffrey Levine, Gerald DeJong, Li-Lun Wang, Rajhans Samdani, Shankar Vembu, Dan Roth

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Label Ranking Algorithms: A Survey.

Shankar Vembu, Thomas Gärtner

Proceedings of the Preference Learning., 2010

2009

On structured output training: hard cases and an efficient alternative.

Thomas Gärtner, Shankar Vembu

Mach. Learn., 2009

Learning to Predict Combinatorial Structures

Shankar Vembu

CoRR, 2009

Probabilistic Structured Predictors.

Shankar Vembu, Thomas Gärtner, Mario Boley

Proceedings of the UAI 2009, 2009

2007

DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO).

Daniel Oberle, Anupriya Ankolekar, Pascal Hitzler, Philipp Cimiano, Michael Sintek, Malte Kiesel, Babak Mougouie, Stephan Baumann, Shankar Vembu, Massimo Romanelli

J. Web Semant., 2007

Semidefinite Ranking on Graphs.

Shankar Vembu, Thomas Gärtner, Stefan Wrobel

Proceedings of the Mining and Learning with Graphs, 2007

2006

On the Use of Topic Models for Word Completion.

Elisabeth Wolf, Shankar Vembu, Tristan Miller

Proceedings of the Advances in Natural Language Processing, 2006

Are we Ready to Embrace the Semantic Web?

Shankar Vembu, Stephan Baumann

Proceedings of the Content-Based Retrieval, 23.04. - 28.04.2006, 2006

2005

Separation of Vocals from Polyphonic Audio Recordings .

Shankar Vembu, Stephan Baumann

Proceedings of the ISMIR 2005, 2005

2004

Towards a Socio-cultural Compatibility of MIR Systems.

Stephan Baumann, Tim Pohle, Shankar Vembu

Proceedings of the ISMIR 2004, 2004

A Self-Organizing Map Based Knowledge Discovery for Music Recommendation Systems.

Shankar Vembu, Stephan Baumann

Proceedings of the Computer Music Modeling and Retrieval: Second International Symposium, 2004