1. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Yadati Narahari: Ballooning Multi-Armed Bandits. International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. (Type: Conference | BibTeX | Tags: ActiveLearning, MAB, online learning) @conference{ghalme20a, title = {Ballooning Multi-Armed Bandits}, author = {Ganesh Ghalme and Swapnil Dhamal and Shweta Jain and Sujit Gujar and Yadati Narahari}, year = {2020}, date = {2020-05-11}, booktitle = {International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020}, keywords = {ActiveLearning, MAB, online learning}, pubstate = {published}, tppubtype = {conference} } |
2. | Kulin Shah; Naresh Manwani: Online Active Learning of Reject Option Classifiers. AAAI, AAAI Press, Forthcoming. (Type: Conference | Links | BibTeX | Tags: ActiveLearning, Online, RejectOption) @conference{Kulin2020, title = {Online Active Learning of Reject Option Classifiers}, author = {Kulin Shah and Naresh Manwani}, url = {https://arxiv.org/abs/1906.06166}, year = {2020}, date = {2020-02-07}, booktitle = {AAAI}, publisher = {AAAI Press}, keywords = {ActiveLearning, Online, RejectOption}, pubstate = {forthcoming}, tppubtype = {conference} } |
3. | Saurabh Ravindranath; Rahul Baburaj; Vineeth Balasubramanian; Nageswararao Namburu; Sujit Gujar; Jawahar C V: Human-Machine Collaboration for Face Recognition. 7th ACM India Joint International Conference on
Data Science & Management of Data, COMAD/CODS, ACM, 2020. (Type: Conference | Abstract | Links | BibTeX | Tags: ActiveLearning, Face recognition, MAB) @conference{Ravindranath20a, title = {Human-Machine Collaboration for Face Recognition}, author = {Saurabh Ravindranath and Rahul Baburaj and Vineeth Balasubramanian and Nageswararao Namburu and Sujit Gujar and C. V. Jawahar}, doi = {https://doi.org/10.1145/3371158.3371160}, year = {2020}, date = {2020-01-05}, booktitle = {7th ACM India Joint International Conference on Data Science & Management of Data, COMAD/CODS}, publisher = {ACM}, abstract = {Despite advances in deep learning and facial recognition techniques, the problem of fault-intolerant facial recognition remains challenging. With the current state of progress in the field of automatic face recognition and the in-feasibility of fully manual recognition, the situation calls for human-machine collaborative methods. We design a system that uses machine predictions for a given face to generate queries that are answered by human experts to provide the system with the information required to predict the identity of the face correctly. We use a Markov Decision Process for which we devise an appropriate query structure and a reward structure to generate these queries in a budget or accuracy-constrained setting. Finally, as we do not know the capabilities of the human experts involved, we model each human as a bandit and adopt a multi-armed bandit approach with consensus queries to efficiently estimate their individual accuracies , enabling us to maximize the accuracy of our system. Through careful analysis and experimentation on real-world data-sets using humans, we show that our system outperforms methods that exploit only machine intelligence, simultaneously being highly cost-efficient as compared to fully manual methods. In summary, our system uses human-machine collaboration for face recognition problem more intelligently and efficiently.}, keywords = {ActiveLearning, Face recognition, MAB}, pubstate = {published}, tppubtype = {conference} } Despite advances in deep learning and facial recognition techniques, the problem of fault-intolerant facial recognition remains challenging. With the current state of progress in the field of automatic face recognition and the in-feasibility of fully manual recognition, the situation calls for human-machine collaborative methods. We design a system that uses machine predictions for a given face to generate queries that are answered by human experts to provide the system with the information required to predict the identity of the face correctly. We use a Markov Decision Process for which we devise an appropriate query structure and a reward structure to generate these queries in a budget or accuracy-constrained setting. Finally, as we do not know the capabilities of the human experts involved, we model each human as a bandit and adopt a multi-armed bandit approach with consensus queries to efficiently estimate their individual accuracies , enabling us to maximize the accuracy of our system. Through careful analysis and experimentation on real-world data-sets using humans, we show that our system outperforms methods that exploit only machine intelligence, simultaneously being highly cost-efficient as compared to fully manual methods. In summary, our system uses human-machine collaboration for face recognition problem more intelligently and efficiently. |
1. | Ballooning Multi-Armed Bandits. International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. | :
2. | Online Active Learning of Reject Option Classifiers. AAAI, AAAI Press, Forthcoming. | :
3. | Human-Machine Collaboration for Face Recognition. 7th ACM India Joint International Conference on Data Science & Management of Data, COMAD/CODS, ACM, 2020. | :