A Self-Organizing Map Based Knowledge Discovery for Music Recommendation Systems. - Shankar Vembu; Stephan Baumann 001 (CMMR, 2004)

Towards a Socio-cultural Compatibility of MIR Systems. - Stephan Baumann 001; Tim Pohle; Shankar Vembu (ISMIR, 2004)

Separation of Vocals from Polyphonic Audio Recordings . - Shankar Vembu; Stephan Baumann 001 (ISMIR, 2005)

Are we Ready to Embrace the Semantic Web? - Shankar Vembu; Stephan Baumann 001 (Content-Based Retrieval, 2006)

On the Use of Topic Models for Word Completion. - Elisabeth Wolf; Shankar Vembu; Tristan Miller (FinTAL, 2006)

Semidefinite Ranking on Graphs. - Shankar Vembu; Thomas Gärtner 001; Stefan Wrobel (MLG, 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 001; Shankar Vembu; Massimo Romanelli (J. Web Semant., 2007)

MAP Structured Prediction by Sampling. - Shankar Vembu; Thomas Gärtner 001; Mario Boley (MLG/SRL@ILP, 2009)

Probabilistic Structured Predictors. - Shankar Vembu; Thomas Gärtner 001; Mario Boley (UAI, 2009)

Learning to Predict Combinatorial Structures - Shankar Vembu (CoRR, 2009)

On structured output training: hard cases and an efficient alternative. - Thomas Gärtner 001; Shankar Vembu (Mach. Learn., 2009)

Label Ranking Algorithms: A Survey. - Shankar Vembu; Thomas Gärtner 001 (Preference Learning, 2010)

Automatic Model Adaptation for Complex Structured Domains. - Geoffrey Levine; Gerald DeJong; Li-Lun Wang; Rajhans Samdani; Shankar Vembu; Dan Roth 001 (ECML/PKDD (2), 2010)

Learning to predict combinatorial structures. - Shankar Vembu (2010)

Dynamical SVM for Time Series Classification. - Ramón Huerta; Shankar Vembu; Mehmet K. Muezzinoglu; Alexander Vergara (DAGM/OAGM Symposium, 2012)

Predicting accurate probabilities with a ranking loss. - Aditya Krishna Menon; Xiaoqian Jiang; Shankar Vembu; Charles Elkan; Lucila Ohno-Machado (ICML, 2012)

Learning and Inference in Probabilistic Classifier Chains with Beam Search. - Abhishek Kumar 004; Shankar Vembu; Aditya Krishna Menon; Charles Elkan (ECML/PKDD (1), 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)

Inhibition in Multiclass Classification. - Ramón Huerta; Shankar Vembu; José M. Amigó; Thomas Nowotny; Charles Elkan (Neural Comput., 2012)

Beam search algorithms for multilabel learning. - Abhishek Kumar 004; Shankar Vembu; Aditya Krishna Menon; Charles Elkan (Mach. Learn., 2013)

An Efficient Algorithm to Integrate Network and Attribute Data for Gene Function Prediction. - Shankar Vembu; Quaid Morris (Pacific Symposium on Biocomputing, 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)

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)

Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors. - Amit G. Deshwar; Shankar Vembu; Quaid Morris (Pacific Symposium on Biocomputing, 2015)

Interactive Learning from Multiple Noisy Labels. - Shankar Vembu; Sandra Zilles (ECML/PKDD (1), 2016)

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)

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)

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)

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; et al. (Nat., 2020)

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)