2020
|
| 71. | Padala Manisha; Sujit Gujar FNNC: Achieving Fairness through Neural Networks Conference The 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, 2020. 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 = {published},
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. |
| 70. | Praveen Paruchuri; Sreeja Kamishetty; Soumya Vadlamannati System and method for controlling vehicular pollution concentration and providing maximum traffic flow throughput Patent 2020. BibTeX @patent{Paruchuri2020,
title = {System and method for controlling vehicular pollution concentration and providing maximum traffic flow throughput },
author = {Praveen Paruchuri and Sreeja Kamishetty and Soumya Vadlamannati},
year = {2020},
date = {2020-06-25},
keywords = {},
pubstate = {published},
tppubtype = {patent}
}
|
| 69. | Rajarshi Bhattacharjee; Naresh Manwani Online Algorithms for Multiclass Classification using Partial Labels Conference PAKDD 2020, 2020. 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 = {published},
tppubtype = {inproceedings}
}
|
| 68. | Shoeb Siddiqui; Sujit Gujar; Ganesh Vanahalli BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model Conference International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. 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 = {published},
tppubtype = {conference}
}
|
| 67. | Kumar Abhishek; Shweta Jain; Sujit Gujar Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions Conference International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, 2020. 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 = {published},
tppubtype = {conference}
}
|
| 66. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Yadati Narahari Ballooning Multi-Armed Bandits Conference International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. 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 = {published},
tppubtype = {conference}
}
|
| 65. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding Workshop Games, Agents, and Incentives Workshop (GAIW@AAMAS 2020), 2020. Links | 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},
url = {http://www.agent-games-2020.preflib.org/wp-content/uploads/2020/05/GAIW2020_paper_9.pdf},
year = {2020},
date = {2020-05-11},
booktitle = {Games, Agents, and Incentives Workshop (GAIW@AAMAS 2020)},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 64. | Moin Hussain Moti; Dimitris Chatzopoulos; Pan Hui; Boi Faltings; Sujit Gujar Orthos: A Trustworthy AI Framework For Data Acquisition Workshop 8th International Workshop on Engineering Multi-Agent Systems (EMAS 2020), 2020. 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 = {published},
tppubtype = {workshop}
}
|
| 63. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Narahari Y Ballooning Multi-Armed Bandits Workshop Adaptive and Learning Agents, 2020. 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 = {published},
tppubtype = {workshop}
}
|
| 62. | Dimitris Chatzopoulos; Sujit Gujar; Boi Faltings; Pan Hui Mneme: A Mobile Distributed Ledger Conference IEEE Conference on Communication, INFOCOM, IEEE, 2020. 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 = {published},
tppubtype = {inproceedings}
}
|
| 61. | Naresh Manwani; Mohit Chandra Exact Passive Aggressive Learning of Ordinal Regression Using Interval Labels Journal Article IEEE Transactions on Neural Networks and Learning Systems (NNLS), 2020. 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 = {published},
tppubtype = {article}
}
|
| 60. | Susobhan Ghosh; Sujit Gujar; Praveen Paruchuri; Easwar Subramanian; Sanjay P Bidding in Smart Grid PDAs: Theory, Analysis and Strategy Conference AAAI, AAAI Press, 2020. 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 = {published},
tppubtype = {inproceedings}
}
|
| 59. | Shweta Jain; Sujit Gujar A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids Conference AAAI, AAAI Press, 2020. 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 = {published},
tppubtype = {inproceedings}
}
|
| 58. | 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}
}
|
| 57. | 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. |
| 56. | 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. |
| 55. | Sreeja Kamishetty; Soumya Vadlamannati; Praveen Paruchuri Towards a better management of urban traffic pollution using a Pareto max flow approach Journal Article Transportation Research Part D: Transport and Environment, 79 , pp. 102194, 2020. BibTeX @article{kamishetty2020towards,
title = {Towards a better management of urban traffic pollution using a Pareto max flow approach},
author = {Sreeja Kamishetty and Soumya Vadlamannati and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
journal = {Transportation Research Part D: Transport and Environment},
volume = {79},
pages = {102194},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| 54. | Sai Krishna Munnangi; Praveen Paruchuri Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach. Conference HICSS, pp. 1–10, 2020. BibTeX @inproceedings{munnangi2020improving,
title = {Improving Wildlife Monitoring using a Multi-criteria Cooperative Target Observation Approach.},
author = {Sai Krishna Munnangi and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {HICSS},
pages = {1–10},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 53. | Anoop Karnik Dasika; Praveen Paruchuri An Ensemble Learning Approach to Improve Tracking Accuracy of Multi Sensor Fusion. Conference ICONIP, 2020. BibTeX @inproceedings{dasika2020an,
title = {An Ensemble Learning Approach to Improve Tracking Accuracy of Multi Sensor Fusion.},
author = {Anoop Karnik Dasika and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {ICONIP},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 52. | Anindya Pradhan; Easwar Subramanian; Sanjay Bhat P; Praveen Paruchuri; Sujit Gujar Rise of Algorithmic Trading in Today’s Changing Electricity Market. Conference India Smart Utility Week, 2020. BibTeX @inproceedings{pradhan2020rise,
title = {Rise of Algorithmic Trading in Today’s Changing Electricity Market.},
author = {Anindya Pradhan and Easwar Subramanian and Sanjay P Bhat and Praveen Paruchuri and Sujit Gujar},
year = {2020},
date = {2020-01-01},
booktitle = {India Smart Utility Week},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 51. | Sai Naveen Pucha; Praveen Paruchuri Inferring Personality Types for Better Automated Negotiation Conference International Conference on Group Decision and Negotiation, pp. 149–162, Springer 2020. BibTeX @inproceedings{pucha2020inferring,
title = {Inferring Personality Types for Better Automated Negotiation},
author = {Sai Naveen Pucha and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {International Conference on Group Decision and Negotiation},
pages = {149–162},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 50. | Aditya Srinivas Gear; Kritika Prakash; Nonidh Singh; Praveen Paruchuri PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value Conference International Conference on Group Decision and Negotiation, pp. 135–148, Springer 2020. BibTeX @inproceedings{gear2020predictrv,
title = {PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value},
author = {Aditya Srinivas Gear and Kritika Prakash and Nonidh Singh and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {International Conference on Group Decision and Negotiation},
pages = {135–148},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 49. | Aditya Srinivas Gear; Kritika Prakash; Nonidh Singh; Praveen Paruchuri PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value Conference Morais, Danielle Costa; Fang, Liping; Horita, Masahide (Ed.): Group Decision and Negotiation: A Multidisciplinary Perspective, pp. 135–148, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-48641-9. Abstract | BibTeX @inproceedings{10.1007/978-3-030-48641-9_10,
title = {PredictRV: A Prediction Based Strategy for Negotiations with Dynamically Changing Reservation Value},
author = {Aditya Srinivas Gear and Kritika Prakash and Nonidh Singh and Praveen Paruchuri},
editor = {Danielle Costa Morais and Liping Fang and Masahide Horita},
isbn = {978-3-030-48641-9},
year = {2020},
date = {2020-01-01},
booktitle = {Group Decision and Negotiation: A Multidisciplinary Perspective},
pages = {135–148},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and Time-Dependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and Time-Dependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings. |
| 48. | Sreeja Kamishetty; Praveen Paruchuri Towards a Better Management of Emergency Evacuation using Pareto Min Cost Max Flow Approach Conference 6th International Conference on Vehicle Technology and Intelligent Transportation Systems, 2020. BibTeX @conference{sreeja2020towards,
title = {Towards a Better Management of Emergency Evacuation using Pareto Min Cost Max Flow Approach},
author = {Sreeja Kamishetty and Praveen Paruchuri},
year = {2020},
date = {2020-01-01},
booktitle = {6th International Conference on Vehicle Technology and Intelligent Transportation Systems},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
2019
|
| 47. | 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}
}
|
| 46. | 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}
}
|
| 45. | Sujit Gujar Blockchain Technology: What is it and What is its future? Book Chapter Manorama Yearbook 2019, Malayala Manorama Press, 2019. BibTeX @inbook{Gujar2019a,
title = {Blockchain Technology: What is it and What is its future?},
author = {Sujit Gujar},
year = {2019},
date = {2019-01-01},
booktitle = {Manorama Yearbook 2019},
publisher = {Malayala Manorama Press},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
|
| 44. | 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}
}
|
| 43. | 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}
}
|
| 42. | 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}
}
|
| 41. | 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}
}
|
| 40. | 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}
}
|
| 39. | 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}
}
|
| 38. | 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}
}
|
| 37. | 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}
}
|
| 36. | 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}
}
|
| 35. | 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}
}
|
| 34. | 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}
}
|
| 33. | 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}
}
|
| 32. | Susobhan Ghosh; Kritika Prakash; Sanjay Chandlekar; 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 Sanjay Chandlekar 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}
}
|
| 31. | Girish Varma; Anbumani Subramanian; Anoop Namboodiri; Manmohan Chandraker; Jawahar C V 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 C V 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
|
| 30. | 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}
}
|
| 29. | 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}
}
|
| 28. | 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}
}
|
| 27. | 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}
}
|
| 26. | 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}
}
|
| 25. | 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}
}
|
| 24. | 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}
}
|
| 23. | 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}
}
|
| 22. | 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}
}
|
