2020
|
| 60. | Padala Manisha; Sujit Gujar FNNC: Achieving Fairness through Neural Networks Conference Forthcoming The 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, Forthcoming. Abstract | BibTeX @conference{manisha2020a,
title = {FNNC: Achieving Fairness through Neural Networks},
author = {Padala Manisha and Sujit Gujar},
year = {2020},
date = {2020-07-11},
booktitle = {The 29th International Joint Conference on Artificial Intelligence, IJCAI 2020},
abstract = {The classification models ensure fairness by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging. We propose a neural network-based framework, \emph{FNNC}, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
The classification models ensure fairness by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging. We propose a neural network-based framework, emph{FNNC}, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints. |
| 59. | Rajarshi Bhattacharjee; Naresh Manwani Online Algorithms for Multiclass Classification using Partial Labels Conference Forthcoming PAKDD 2020, Forthcoming. Links | BibTeX @inproceedings{Rajarshi2020,
title = {Online Algorithms for Multiclass Classification using Partial Labels},
author = {Rajarshi Bhattacharjee and Naresh Manwani},
url = {https://arxiv.org/abs/1912.11367},
year = {2020},
date = {2020-05-14},
booktitle = {PAKDD 2020},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 58. | Shoeb Siddiqui; Sujit Gujar; Ganesh Vanahalli BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model Conference Forthcoming International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, Forthcoming. BibTeX @conference{shoeb20a,
title = {BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model},
author = {Shoeb Siddiqui and Sujit Gujar and Ganesh Vanahalli},
year = {2020},
date = {2020-05-11},
booktitle = {International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 57. | Kumar Abhishek; Shweta Jain; Sujit Gujar Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions Conference Forthcoming International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, Forthcoming. BibTeX @conference{abhishesk20a,
title = {Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions},
author = {Kumar Abhishek and Shweta Jain and Sujit Gujar },
year = {2020},
date = {2020-05-11},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 56. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Yadati Narahari Ballooning Multi-Armed Bandits Conference Forthcoming International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, Forthcoming. BibTeX @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 = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 55. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding Workshop Forthcoming Games, Agents, and Incentives Workshop (GAIW@AAMAS 2020), Forthcoming. BibTeX @workshop{Damle2020,
title = {Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding},
author = {Sankarshan Damle and Moin Hussain Moti and Praphul Chandra and Sujit Gujar},
editor = {Sankarshan Damle},
year = {2020},
date = {2020-05-11},
booktitle = {Games, Agents, and Incentives Workshop (GAIW@AAMAS 2020)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workshop}
}
|
| 54. | Moin Hussain Moti; Dimitris Chatzopoulos; Pan Hui; Boi Faltings; Sujit Gujar Orthos: A Trustworthy AI Framework For Data Acquisition Workshop Forthcoming 8th International Workshop on Engineering Multi-Agent Systems (EMAS 2020), Forthcoming. BibTeX @workshop{Moti2020,
title = {Orthos: A Trustworthy AI Framework For Data Acquisition},
author = {Moin Hussain Moti and Dimitris Chatzopoulos and Pan Hui and Boi Faltings and Sujit Gujar },
editor = {Moin Hussain Moti},
year = {2020},
date = {2020-05-11},
booktitle = {8th International Workshop on Engineering Multi-Agent Systems (EMAS 2020)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workshop}
}
|
| 53. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Narahari Y Ballooning Multi-Armed Bandits Workshop Forthcoming Adaptive and Learning Agents, Forthcoming. BibTeX @workshop{Ghalme20b,
title = {Ballooning Multi-Armed Bandits},
author = {Ganesh Ghalme and Swapnil Dhamal and Shweta Jain and Sujit Gujar and Y Narahari},
year = {2020},
date = {2020-05-09},
booktitle = {Adaptive and Learning Agents},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workshop}
}
|
| 52. | Dimitris Chatzopoulos; Sujit Gujar; Boi Faltings; Pan Hui Mneme: A Mobile Distributed Ledger Conference Forthcoming IEEE Conference on Communication, INFOCOM, IEEE, Forthcoming. BibTeX @inproceedings{dimhatzo20a,
title = {Mneme: A Mobile Distributed Ledger},
author = {Dimitris Chatzopoulos and Sujit Gujar and Boi Faltings and Pan Hui},
year = {2020},
date = {2020-04-27},
booktitle = {IEEE Conference on Communication, INFOCOM},
publisher = {IEEE},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 51. | Naresh Manwani; Mohit Chandra Exact Passive Aggressive Learning of Ordinal Regression Using Interval Labels Journal Article Forthcoming IEEE Transactions on Neural Networks and Learning Systems (NNLS), Forthcoming. Links | BibTeX @article{Naresh_NNLS2020,
title = {Exact Passive Aggressive Learning of Ordinal Regression Using Interval Labels},
author = {Naresh Manwani and Mohit Chandra},
url = {https://ieeexplore.ieee.org/document/8867949},
year = {2020},
date = {2020-02-29},
journal = {IEEE Transactions on Neural Networks and Learning Systems (NNLS)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
|
| 50. | Susobhan Ghosh; Sujit Gujar; Praveen Paruchuri; Easwar Subramanian; Sanjay P Bidding in Smart Grid PDAs: Theory, Analysis and Strategy Conference Forthcoming AAAI, AAAI Press, Forthcoming. Links | BibTeX @inproceedings{ghosh20a,
title = {Bidding in Smart Grid PDAs: Theory, Analysis and Strategy},
author = {Susobhan Ghosh and Sujit Gujar and Praveen Paruchuri and Easwar Subramanian and Sanjay P},
url = {https://www.researchgate.net/publication/337337754_Bidding_in_Smart_Grid_PDAs_Theor},
year = {2020},
date = {2020-02-07},
booktitle = {AAAI},
publisher = {AAAI Press},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 49. | Shweta Jain; Sujit Gujar A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids Conference Forthcoming AAAI, AAAI Press, Forthcoming. BibTeX @inproceedings{sjain20a,
title = {A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids},
author = {Shweta Jain and Sujit Gujar},
year = {2020},
date = {2020-02-07},
booktitle = {AAAI},
publisher = {AAAI Press},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 48. | Kulin Shah; Naresh Manwani Online Active Learning of Reject Option Classifiers Conference Forthcoming AAAI, AAAI Press, Forthcoming. Links | BibTeX @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 = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 47. | Himanshu Kumar; Naresh Manwani; Sastry P S Robust Learning of Multi-Label Classifiers under Label Noise Conference ACM India Joint International Conference on Data Science & Management of Data, 2020. Abstract | Links | BibTeX @conference{Himanshu_CoDS2020,
title = {Robust Learning of Multi-Label Classifiers under Label Noise},
author = {Himanshu Kumar and Naresh Manwani and P.S. Sastry},
doi = {https://doi.org/10.1145/3371158.3371169},
year = {2020},
date = {2020-01-07},
booktitle = {ACM India Joint International Conference on Data Science & Management of Data},
pages = {90-97},
abstract = {In this paper, we address the problem of robust learning of multi-label classifiers when the training data has label noise. We consider learning algorithms in the risk-minimization framework. We define what we call symmetric label noise in multi-label settings which is a useful noise model for many random errors in the labeling of data. We prove that risk minimization is robust to symmetric label noise if the loss function satisfies some conditions. We show that Hamming loss and a surrogate of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feedforward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on the robust learning of multi-label classifiers under label noise.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In this paper, we address the problem of robust learning of multi-label classifiers when the training data has label noise. We consider learning algorithms in the risk-minimization framework. We define what we call symmetric label noise in multi-label settings which is a useful noise model for many random errors in the labeling of data. We prove that risk minimization is robust to symmetric label noise if the loss function satisfies some conditions. We show that Hamming loss and a surrogate of Hamming loss satisfy these sufficient conditions and hence are robust. By learning feedforward neural networks on some benchmark multi-label datasets, we provide empirical evidence to illustrate our theoretical results on the robust learning of multi-label classifiers under label noise. |
| 46. | Saurabh Ravindranath; Rahul Baburaj; Vineeth Balasubramanian; Nageswararao Namburu; Sujit Gujar; Jawahar C V Human-Machine Collaboration for Face Recognition Conference 7th ACM India Joint International Conference on
Data Science & Management of Data, COMAD/CODS, ACM, 2020. Abstract | Links | BibTeX @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 = {},
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. |
2019
|
| 45. | teja Vooturi; Girish Varma; Kishore Kothapalli Dynamic Block Sparse Reparameterization of Convolutional Neural Networks Conference The IEEE International Conference on Computer Vision (ICCV) Workshops, 2019. BibTeX @inproceedings{Vooturi_2019_ICCV_Workshops,
title = {Dynamic Block Sparse Reparameterization of Convolutional Neural Networks},
author = {Dharma teja Vooturi and Girish Varma and Kishore Kothapalli},
year = {2019},
date = {2019-10-01},
booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 44. | Tarun Kalluri; Girish Varma; Manmohan Chandraker; Jawahar C V Universal Semi-Supervised Semantic Segmentation Conference The IEEE International Conference on Computer Vision (ICCV), 2019. BibTeX @inproceedings{Kalluri_2019_ICCV,
title = {Universal Semi-Supervised Semantic Segmentation},
author = {Tarun Kalluri and Girish Varma and Manmohan Chandraker and C V Jawahar},
year = {2019},
date = {2019-10-01},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 43. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs Conference IJCAI, pp. 208–214, ijcai.org, 2019. Links | BibTeX @inproceedings{DBLP:conf/ijcai/DamleMCG19,
title = {Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs},
author = {Sankarshan Damle and Moin Hussain Moti and Praphul Chandra and Sujit Gujar},
url = {http://mll.iiit.ac.in/publication-blogs/ppsn/
},
year = {2019},
date = {2019-01-01},
booktitle = {IJCAI},
pages = {208–214},
publisher = {ijcai.org},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 42. | Moin Hussain Moti; Dimitris Chatzopoulos; Pan Hui; Sujit Gujar FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings Conference IJCAI, pp. 506–512, ijcai.org, 2019. Links | BibTeX @inproceedings{DBLP:conf/ijcai/MotiCHG19,
title = {FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings},
author = {Moin Hussain Moti and Dimitris Chatzopoulos and Pan Hui and Sujit Gujar},
url = {http://mll.iiit.ac.in/publication-blogs/farm/},
doi = {https://doi.org/10.24963/ijcai.2019/72},
year = {2019},
date = {2019-01-01},
booktitle = {IJCAI},
pages = {506–512},
publisher = {ijcai.org},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 41. | Reza Hadi Mogavi; Sujit Gujar; Xiaojuan Ma; Pan Hui HRCR: Hidden Markov-Based Reinforcement to Reduce Churn in Question
Answering Forums Conference PRICAI (1), pp. 364–376, Springer, 2019. BibTeX @inproceedings{DBLP:conf/pricai/MogaviGMH19,
title = {HRCR: Hidden Markov-Based Reinforcement to Reduce Churn in Question
Answering Forums},
author = {Reza Hadi Mogavi and Sujit Gujar and Xiaojuan Ma and Pan Hui},
year = {2019},
date = {2019-01-01},
booktitle = {PRICAI (1)},
volume = {11670},
pages = {364–376},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 40. | Sankarshan Damle; Boi Faltings; Sujit Gujar A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders Conference AAMAS, pp. 1916–1918, International Foundation for Autonomous Agents and Multiagent Systems, 2019. BibTeX @inproceedings{DBLP:conf/atal/DamleFG19,
title = {A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders},
author = {Sankarshan Damle and Boi Faltings and Sujit Gujar},
year = {2019},
date = {2019-01-01},
booktitle = {AAMAS},
pages = {1916–1918},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 39. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar Aggregating Citizen Preferences for Public Projects Through Civic Crowdfunding Conference AAMAS, pp. 1919–1921, International Foundation for Autonomous Agents and Multiagent Systems, 2019. BibTeX @inproceedings{DBLP:conf/atal/DamleMCG19,
title = {Aggregating Citizen Preferences for Public Projects Through Civic Crowdfunding},
author = {Sankarshan Damle and Moin Hussain Moti and Praphul Chandra and Sujit Gujar},
year = {2019},
date = {2019-01-01},
booktitle = {AAMAS},
pages = {1919–1921},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 38. | Padala Manisha; Sujit Gujar Thompson Sampling Based Multi-Armed-Bandit Mechanism Using Neural Networks Conference AAMAS, pp. 2111–2113, International Foundation for Autonomous Agents and Multiagent Systems, 2019. BibTeX @inproceedings{DBLP:conf/atal/ManishaG19,
title = {Thompson Sampling Based Multi-Armed-Bandit Mechanism Using Neural Networks},
author = {Padala Manisha and Sujit Gujar},
year = {2019},
date = {2019-01-01},
booktitle = {AAMAS},
pages = {2111–2113},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 37. | Vaibhav Gupta; Daksh Anand; Praveen Paruchuri; Balaraman Ravindran Advice Replay Approach for Richer Knowledge Transfer in Teacher Student
Framework Conference AAMAS, pp. 1997–1999, International Foundation for Autonomous Agents and Multiagent Systems, 2019. BibTeX @inproceedings{DBLP:conf/atal/GuptaAPR19,
title = {Advice Replay Approach for Richer Knowledge Transfer in Teacher Student
Framework},
author = {Vaibhav Gupta and Daksh Anand and Praveen Paruchuri and Balaraman Ravindran},
year = {2019},
date = {2019-01-01},
booktitle = {AAMAS},
pages = {1997–1999},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 36. | Susobhan Ghosh; Easwar Subramanian; Sanjay Bhat P; Sujit Gujar; Praveen Paruchuri VidyutVanika: A Reinforcement Learning Based Broker Agent for a
Power Trading Competition Conference AAAI, pp. 914–921, AAAI Press, 2019. BibTeX @inproceedings{DBLP:conf/aaai/GhoshSBGP19,
title = {VidyutVanika: A Reinforcement Learning Based Broker Agent for a
Power Trading Competition},
author = {Susobhan Ghosh and Easwar Subramanian and Sanjay P Bhat and Sujit Gujar and Praveen Paruchuri},
year = {2019},
date = {2019-01-01},
booktitle = {AAAI},
pages = {914–921},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 35. | Tarun Gupta; Akshat Kumar; Praveen Paruchuri Successor Features Based Multi-Agent RL for Event-Based Decentralized
MDPs Conference AAAI, pp. 6054–6061, AAAI Press, 2019. BibTeX @inproceedings{DBLP:conf/aaai/0002KP19,
title = {Successor Features Based Multi-Agent RL for Event-Based Decentralized
MDPs},
author = {Tarun Gupta and Akshat Kumar and Praveen Paruchuri},
year = {2019},
date = {2019-01-01},
booktitle = {AAAI},
pages = {6054–6061},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 34. | Kulin Shah; Naresh Manwani Sparse Reject Option Classifier Using Successive Linear Programming Conference AAAI, pp. 4870–4877, AAAI Press, 2019. BibTeX @inproceedings{DBLP:conf/aaai/ShahM19,
title = {Sparse Reject Option Classifier Using Successive Linear Programming},
author = {Kulin Shah and Naresh Manwani},
year = {2019},
date = {2019-01-01},
booktitle = {AAAI},
pages = {4870–4877},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 33. | Satyanath Bhat; Shweta Jain; Sujit Gujar; Narahari Y An optimal bidimensional multi-armed bandit auction for multi-unit
procurement Journal Article Ann. Math. Artif. Intell., 85 (1), pp. 1–19, 2019. BibTeX @article{DBLP:journals/amai/BhatJGN19,
title = {An optimal bidimensional multi-armed bandit auction for multi-unit
procurement},
author = {Satyanath Bhat and Shweta Jain and Sujit Gujar and Y Narahari},
year = {2019},
date = {2019-01-01},
journal = {Ann. Math. Artif. Intell.},
volume = {85},
number = {1},
pages = {1–19},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 32. | Naresh Manwani PRIL: Perceptron Ranking Using Interval Labels Conference COMAD/CODS, pp. 78–85, ACM, 2019. BibTeX @inproceedings{DBLP:conf/comad/Manwani19,
title = {PRIL: Perceptron Ranking Using Interval Labels},
author = {Naresh Manwani},
year = {2019},
date = {2019-01-01},
booktitle = {COMAD/CODS},
pages = {78–85},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 31. | Susobhan Ghosh; Kritika Prakash; Easwar Subramanian; Sanjay Bhat P; Sujit Gujar; Praveen Paruchuri Vidyutvanika: An autonomous broker agent for smart grid environment Conference PASS, Policy, Awareness, Sustainability and Systems (PASS) Workshop, 2019. BibTeX @inproceedings{ghosh2019vidyutvanika,
title = {Vidyutvanika: An autonomous broker agent for smart grid environment},
author = {Susobhan Ghosh and Kritika Prakash and Easwar Subramanian and Sanjay P Bhat and Sujit Gujar and Praveen Paruchuri},
year = {2019},
date = {2019-01-01},
booktitle = {PASS},
volume = {7},
publisher = {Policy, Awareness, Sustainability and Systems (PASS) Workshop},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 30. | Girish Varma; Anbumani Subramanian; Anoop Namboodiri; Manmohan Chandraker; CV. Jawahar IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments Conference 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE 2019. BibTeX @inproceedings{anue,
title = {IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments},
author = {Girish Varma and Anbumani Subramanian and Anoop Namboodiri and Manmohan Chandraker and CV. Jawahar},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2018
|
| 29. | Soham Saha; Girish Varma; Jawahar C V Class2Str: End to End Latent Hierarchy Learning Conference International Conference on Pattern Recognision (ICPR). year = 2018, 2018. BibTeX @inproceedings{SVJ18b,
title = {Class2Str: End to End Latent Hierarchy Learning},
author = {Soham Saha and Girish Varma and C V Jawahar},
year = {2018},
date = {2018-08-20},
booktitle = {International Conference on Pattern Recognision (ICPR). year = 2018},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 28. | Nikitha Vallurupalli; Sriharsha Annamaneni; Girish Varma; Jawahar C V; Manu Mathew; Soyeb Nagori Efficient Semantic Segmentation Using Gradual Grouping Conference The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018. BibTeX @inproceedings{Vallurupalli_2018_CVPR_Workshops,
title = {Efficient Semantic Segmentation Using Gradual Grouping},
author = {Nikitha Vallurupalli and Sriharsha Annamaneni and Girish Varma and C V Jawahar and Manu Mathew and Soyeb Nagori},
year = {2018},
date = {2018-06-01},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 27. | Shweta Jain; Sujit Gujar; Satyanath Bhat; Onno Zoeter; Narahari Y A quality assuring, cost optimal multi-armed bandit mechanism for
expertsourcing Journal Article Artif. Intell., 254 , pp. 44–63, 2018. BibTeX @article{DBLP:journals/ai/JainGBZN18,
title = {A quality assuring, cost optimal multi-armed bandit mechanism for
expertsourcing},
author = {Shweta Jain and Sujit Gujar and Satyanath Bhat and Onno Zoeter and Y Narahari},
year = {2018},
date = {2018-01-01},
journal = {Artif. Intell.},
volume = {254},
pages = {44–63},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 26. | Ganesh Ghalme; Sujit Gujar; Amleshwar Kumar; Shweta Jain; Narahari Y Design of Coalition Resistant Credit Score Functions for Online Discussion
Forums Conference AAMAS, pp. 95–103, International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC, USA / ACM, 2018. BibTeX @inproceedings{DBLP:conf/atal/GhalmeGKJN18,
title = {Design of Coalition Resistant Credit Score Functions for Online Discussion
Forums},
author = {Ganesh Ghalme and Sujit Gujar and Amleshwar Kumar and Shweta Jain and Y Narahari},
year = {2018},
date = {2018-01-01},
booktitle = {AAMAS},
pages = {95–103},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC, USA / ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 25. | Padala Manisha; Jawahar C V; Sujit Gujar Learning Optimal Redistribution Mechanisms Through Neural Networks Conference AAMAS, pp. 345–353, International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, 2018. BibTeX @inproceedings{DBLP:conf/atal/ManishaJG18,
title = {Learning Optimal Redistribution Mechanisms Through Neural Networks},
author = {Padala Manisha and C V Jawahar and Sujit Gujar},
year = {2018},
date = {2018-01-01},
booktitle = {AAMAS},
pages = {345–353},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 24. | Dimitris Chatzopoulos; Sujit Gujar; Boi Faltings; Pan Hui Privacy Preserving and Cost Optimal Mobile Crowdsensing Using Smart
Contracts on Blockchain Conference MASS, pp. 442–450, IEEE Computer Society, 2018. BibTeX @inproceedings{DBLP:conf/mass/ChatzopoulosGFH18,
title = {Privacy Preserving and Cost Optimal Mobile Crowdsensing Using Smart
Contracts on Blockchain},
author = {Dimitris Chatzopoulos and Sujit Gujar and Boi Faltings and Pan Hui},
year = {2018},
date = {2018-01-01},
booktitle = {MASS},
pages = {442–450},
publisher = {IEEE Computer Society},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 23. | Subba Reddy Oota; Naresh Manwani; Raju Bapi S fMRI Semantic Category Decoding Using Linguistic Encoding of Word
Embeddings Conference ICONIP (3), pp. 3–15, Springer, 2018. BibTeX @inproceedings{DBLP:conf/iconip/OotaMB18,
title = {fMRI Semantic Category Decoding Using Linguistic Encoding of Word
Embeddings},
author = {Subba Reddy Oota and Naresh Manwani and Raju S Bapi},
year = {2018},
date = {2018-01-01},
booktitle = {ICONIP (3)},
volume = {11303},
pages = {3–15},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 22. | Tarun Gupta; Akshat Kumar; Praveen Paruchuri Planning and Learning for Decentralized MDPs with Event Driven Rewards Conference AAAI Workshops, pp. 665, AAAI Press, 2018. BibTeX @inproceedings{DBLP:conf/aaai/GuptaKP18a,
title = {Planning and Learning for Decentralized MDPs with Event Driven Rewards},
author = {Tarun Gupta and Akshat Kumar and Praveen Paruchuri},
year = {2018},
date = {2018-01-01},
booktitle = {AAAI Workshops},
volume = {WS-18},
pages = {665},
publisher = {AAAI Press},
series = {AAAI Workshops},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 21. | Tarun Gupta; Akshat Kumar; Praveen Paruchuri Planning and Learning for Decentralized MDPs With Event Driven Rewards Conference AAAI, pp. 6186–6194, AAAI Press, 2018. BibTeX @inproceedings{DBLP:conf/aaai/GuptaKP18b,
title = {Planning and Learning for Decentralized MDPs With Event Driven Rewards},
author = {Tarun Gupta and Akshat Kumar and Praveen Paruchuri},
year = {2018},
date = {2018-01-01},
booktitle = {AAAI},
pages = {6186–6194},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 20. | Praveen Paruchuri; Sujit Gujar Fusion of Game Theory and Big Data for AI Applications Conference BDA, pp. 55–69, Springer, 2018. BibTeX @inproceedings{DBLP:conf/bigda/ParuchuriG18,
title = {Fusion of Game Theory and Big Data for AI Applications},
author = {Praveen Paruchuri and Sujit Gujar},
year = {2018},
date = {2018-01-01},
booktitle = {BDA},
volume = {11297},
pages = {55–69},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 19. | Gaurav Gupta; Sreeja Kamishetty; Praveen Paruchuri A Prioritized Routing Assistant for Flow of Traffic Conference ITSC, pp. 3627–3632, IEEE, 2018. BibTeX @inproceedings{DBLP:conf/itsc/GuptaKP18,
title = {A Prioritized Routing Assistant for Flow of Traffic},
author = {Gaurav Gupta and Sreeja Kamishetty and Praveen Paruchuri},
year = {2018},
date = {2018-01-01},
booktitle = {ITSC},
pages = {3627–3632},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 18. | Ameya Prabhu; Girish Varma; Anoop Namboodiri Deep Expander Networks: Efficient Deep Networks from Graph Theory Conference Ferrari, Vittorio; Hebert, Martial; Sminchisescu, Cristian; Weiss, Yair (Ed.): Computer Vision — ECCV 2018, pp. 20–36, Springer International Publishing, Cham, 2018, ISBN: 978-3-030-01261-8. BibTeX @inproceedings{PVN18,
title = {Deep Expander Networks: Efficient Deep Networks from Graph Theory},
author = {Ameya Prabhu and Girish Varma and Anoop Namboodiri},
editor = {Vittorio Ferrari and Martial Hebert and Cristian Sminchisescu and Yair Weiss},
isbn = {978-3-030-01261-8},
year = {2018},
date = {2018-01-01},
booktitle = {Computer Vision — ECCV 2018},
pages = {20–36},
publisher = {Springer International Publishing},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 17. | Soham Saha; Girish Varma; Jawahar C V Improved Visual Relocalization by Discovering Anchor Points Conference British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018, pp. 164, 2018. Links | BibTeX @inproceedings{SVJ18,
title = {Improved Visual Relocalization by Discovering Anchor Points},
author = {Soham Saha and Girish Varma and C V Jawahar},
url = {http://bmvc2018.org/contents/papers/0962.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {British Machine Vision Conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, 2018},
pages = {164},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 16. | Sudhir Yarram; Girish Varma; Jawahar C V City-Scale Road Audit System using Deep Learning Conference International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018, 2018. BibTeX @inproceedings{YVJ18,
title = {City-Scale Road Audit System using Deep Learning},
author = {Sudhir Yarram and Girish Varma and C V Jawahar},
year = {2018},
date = {2018-01-01},
booktitle = {International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2017
|
| 15. | Ganesh Ghalme; Shweta Jain; Sujit Gujar; Narahari Y Thompson Sampling Based Mechanisms for Stochastic Multi-Armed Bandit
Problems Conference AAMAS, pp. 87–95, ACM, 2017. BibTeX @inproceedings{DBLP:conf/atal/GhalmeJGN17,
title = {Thompson Sampling Based Mechanisms for Stochastic Multi-Armed Bandit
Problems},
author = {Ganesh Ghalme and Shweta Jain and Sujit Gujar and Y Narahari},
year = {2017},
date = {2017-01-01},
booktitle = {AAMAS},
pages = {87–95},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 14. | Praphul Chandra; Sujit Gujar; Yadati Narahari Referral-Embedded Provision Point Mechanisms for Crowdfunding of Public
Projects Conference AAMAS, pp. 642–650, ACM, 2017. BibTeX @inproceedings{DBLP:conf/atal/ChandraGN17,
title = {Referral-Embedded Provision Point Mechanisms for Crowdfunding of Public
Projects},
author = {Praphul Chandra and Sujit Gujar and Yadati Narahari},
year = {2017},
date = {2017-01-01},
booktitle = {AAMAS},
pages = {642–650},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 13. | Aritra Ghosh; Naresh Manwani; Sastry P S On the Robustness of Decision Tree Learning Under Label Noise Conference PAKDD (1), pp. 685–697, 2017. BibTeX @inproceedings{DBLP:conf/pakdd/GhoshMS17,
title = {On the Robustness of Decision Tree Learning Under Label Noise},
author = {Aritra Ghosh and Naresh Manwani and P S Sastry},
year = {2017},
date = {2017-01-01},
booktitle = {PAKDD (1)},
volume = {10234},
pages = {685–697},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 12. | Rashi Aswani; Sai Krishna Munnangi; Praveen Paruchuri Improving Surveillance Using Cooperative Target Observation Conference AAAI, pp. 2985–2991, AAAI Press, 2017. BibTeX @inproceedings{DBLP:conf/aaai/AswaniMP17,
title = {Improving Surveillance Using Cooperative Target Observation},
author = {Rashi Aswani and Sai Krishna Munnangi and Praveen Paruchuri},
year = {2017},
date = {2017-01-01},
booktitle = {AAAI},
pages = {2985–2991},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 11. | Sujit Gujar; Boi Faltings Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing Book Chapter Ceppi, Sofia; David, Esther; Hajaj, Chen; Robu, Valentin; Vetsikas, Ioannis A (Ed.): pp. 50–65, Springer International Publishing, Cham, 2017, ISBN: 978-3-319-54229-4, (Revised Selected Papers from AMEC/TADA 2015). Abstract | BibTeX @inbook{10.1007/978-3-319-54229-4_4,
title = {Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing},
author = {Sujit Gujar and Boi Faltings},
editor = {Sofia Ceppi and Esther David and Chen Hajaj and Valentin Robu and Ioannis A Vetsikas},
isbn = {978-3-319-54229-4},
year = {2017},
date = {2017-01-01},
pages = {50–65},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal workers for a given set of tasks. However, workers also have preferences and will stay with a platform only if it gives them interesting tasks. We therefore analyze several matching mechanisms that take into account workers’ preferences as well. We propose that the workers pay premiums to get preferred matches and auction-based models where preferences are expressed through variations of the payment for a task. We analyze the properties of two matching different mechanisms: Split Dynamic VCG (SDV) and e-Auction. We compare both the mechanisms with Arrival Priority Serial Dictatorship (APSD) empirically for efficiency.},
note = {Revised Selected Papers from AMEC/TADA 2015},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal workers for a given set of tasks. However, workers also have preferences and will stay with a platform only if it gives them interesting tasks. We therefore analyze several matching mechanisms that take into account workers’ preferences as well. We propose that the workers pay premiums to get preferred matches and auction-based models where preferences are expressed through variations of the payment for a task. We analyze the properties of two matching different mechanisms: Split Dynamic VCG (SDV) and e-Auction. We compare both the mechanisms with Arrival Priority Serial Dictatorship (APSD) empirically for efficiency. |
