34 citations · 65 across the 9 of their papers we have counts for
9 papers
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition
Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan +2
Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, includin…
Implementing Recommendation Algorithms in a Large-Scale Biomedical Science Knowledge Base
Jessica Perrie, Yanqi Hao, Zack Hayat +4
The number of biomedical research articles published has doubled in the past 20 years. Search engine based systems naturally center around searching, but researchers may not have a…
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…
Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors
Amit G. Deshwar, Shankar Vembu, Quaid Morris
Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe t…
Reconstructing subclonal composition and evolution from whole genome sequencing of tumors
Amit G. Deshwar, Shankar Vembu, Christina K. Yung +3
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, that can be applied to WGS data from one…
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…