Maanik Arora received his MS Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Dr. Naresh Manwani. Here’s a summary of his research work on Learning multiclass classified under uncertain/incomplete supervision:
Online learning is a very well worked learning paradigm that has both theoretical as well as practical appeal. Online learning aims to make a sequence of accurate predictions given the correct answer to previous prediction tasks and possibly additional available information. One of the subproblems of great interest in this field of learning is multiclass classification in online settings. Multiclass classification can be understood as predicting the best label, out of a given number of labels, for a particular instance.
In this thesis, we address the problem of multiclass classification in an online setting. Notably, we investigate the case of learning in limited feedback (partial and bandit) from the environment in online fashion. We may not be given exact class labels or ground truths for training instances in many real-life classification problems. Instead, we could be given a set of candidate labels containing the true label or binary feedback indicating whether our prediction is correct or not. In both of these scenarios, we are left in uncertainty about the actual class label.
This thesis will do a literature survey and a brief study of the current work done. We will then propose the solutions to the problems mentioned above with thorough mathematical analysis, including regret bounds and experimental evidences suggesting the correctness and efficiency of the proposed methods.