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Imputation of Unknown Missingness in Sparse Electronic Health Records
Jun Han, Josue Nassar, Sanjit Singh Batra +3
Machine learning holds great promise for advancing the field of medicine, with electronic health records (EHRs) serving as a primary data source. However, EHRs are often sparse and…
Fast and Effective On-policy Distillation from Reasoning Prefixes
Dongxu Zhang, Zhichao Yang, Sepehr Janghorbani +6
On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token level, avoids relying solely on verifiable terminal…
Feature Selection for classification of hyperspectral data by minimizing a tight bound on the VC dimension
Phool Preet, Sanjit Singh Batra, Jayadeva
Hyperspectral data consists of large number of features which require sophisticated analysis to be extracted. A popular approach to reduce computational cost, facilitate informatio…
Learning a Fuzzy Hyperplane Fat Margin Classifier with Minimum VC dimension
Jayadeva, Sanjit Singh Batra, Siddarth Sabharwal
The Vapnik-Chervonenkis (VC) dimension measures the complexity of a learning machine, and a low VC dimension leads to good generalization. The recently proposed Minimal Complexity…
Feature Selection through Minimization of the VC dimension
Jayadeva, Sanjit S. Batra, Siddharth Sabharwal
Feature selection involes identifying the most relevant subset of input features, with a view to improving generalization of predictive models by reducing overfitting. Directly sea…
Learning a hyperplane regressor by minimizing an exact bound on the VC dimension
Jayadeva, Suresh Chandra, Siddarth Sabharwal +1
The capacity of a learning machine is measured by its Vapnik-Chervonenkis dimension, and learning machines with a low VC dimension generalize better. It is well known that the VC d…