34 citations · 65 across the 9 of their papers we have counts for
5 papers · 1 filter
Interactive Learning from Multiple Noisy Labels
Shankar Vembu, Sandra Zilles
Interactive learning is a process in which a machine learning algorithm is provided with meaningful, well-chosen examples as opposed to randomly chosen examples typical in standard…
Inferring clonal evolution of tumors from single nucleotide somatic mutations
Wei Jiao, Shankar Vembu, Amit G. Deshwar +2
High-throughput sequencing allows the detection and quantification of frequencies of somatic single nucleotide variants (SNV) in heterogeneous tumor cell populations. In some cases…
Predicting accurate probabilities with a ranking loss
Aditya Menon, Xiaoqian Jiang, Shankar Vembu +2
In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a sim…
Probabilistic Structured Predictors
Shankar Vembu, Thomas Gartner, Mario Boley
We consider MAP estimators for structured prediction with exponential family models. In particular, we concentrate on the case that efficient algorithms for uniform sampling from t…
Learning to Predict Combinatorial Structures
Shankar Vembu
The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the…