2021
|
| 86. | Sarath Sivaprasad; Ankur Singh; Naresh Manwani; Vineet Gandhi The Curious Case of Convex Networks Conference Forthcoming ECML 2021, Forthcoming. BibTeX @conference{Sarath-et-al-2021,
title = {The Curious Case of Convex Networks},
author = {Sarath Sivaprasad and Ankur Singh and Naresh Manwani and Vineet Gandhi},
year = {2021},
date = {2021-09-17},
booktitle = {ECML 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 85. | Bhavya Kalra; Naresh Manwani RISAN: Robust Instance Specific Deep Abstention Network Conference Forthcoming UAI 2021, Forthcoming. BibTeX @conference{Bhavya-Naresh2021,
title = {RISAN: Robust Instance Specific Deep Abstention Network},
author = {Bhavya Kalra and Naresh Manwani},
year = {2021},
date = {2021-07-29},
booktitle = {UAI 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 84. | Jaikumar Radhakrishnan Girish Varma Prerona Chatterjee Kshitij Gajjar Generalized Parametric Path Problems Conference Forthcoming 37th Conference on Uncertainty in Artificial Intelligence (UAI) 2021, Forthcoming. BibTeX @conference{Chatterjee2021,
title = {Generalized Parametric Path Problems},
author = {Prerona Chatterjee, Kshitij Gajjar, Jaikumar Radhakrishnan, Girish Varma},
year = {2021},
date = {2021-07-27},
booktitle = {37th Conference on Uncertainty in Artificial Intelligence (UAI) 2021},
journal = {Proceedings of Machine Learning Research},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 83. | Mudit Agarwal; Naresh Manwani Learning Multiclass Classifier Under Noisy Bandit Feedback Conference Forthcoming PAKDD 2021, Forthcoming. BibTeX @conference{Mudit2021,
title = {Learning Multiclass Classifier Under Noisy Bandit Feedback},
author = {Mudit Agarwal and Naresh Manwani},
year = {2021},
date = {2021-05-15},
booktitle = {PAKDD 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 82. | Bhavya Kalra; Sai Krishna Munnangi; Kushal Majmundar; Naresh Manwani; Praveen Paruchuri Cooperative Monitoring of Malicious Activity in Stock Exchanges Workshop Workshop on Data Assessment and Readiness for Artificial Intelligence, PAKDD 2021, 2021. BibTeX @workshop{Bhavya-et.-al-2021,
title = {Cooperative Monitoring of Malicious Activity in Stock Exchanges},
author = {Bhavya Kalra and Sai Krishna Munnangi and Kushal Majmundar and Naresh Manwani and Praveen Paruchuri},
year = {2021},
date = {2021-05-15},
booktitle = {Workshop on Data Assessment and Readiness for Artificial Intelligence, PAKDD 2021},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 81. | Anurag Jain; Shoeb Siddiqui; Sujit Gujar We might walk together, but I run faster: Network Fairness and Scalability in Blockchains Conference Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), 2021. Abstract | BibTeX @conference{Jain2021,
title = {We might walk together, but I run faster: Network Fairness and Scalability in Blockchains},
author = {Anurag Jain and Shoeb Siddiqui and Sujit Gujar},
editor = {U. Endriss and A. Now\'{e} and F. Dignum and A. Lomuscio },
year = {2021},
date = {2021-05-05},
booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)},
abstract = {Blockchain-based Distributed Ledgers (DLs) promise to transform the existing financial system by making it truly democratic. In the past decade, blockchain technology has seen many novel applications ranging from the banking industry to real estate. However, in order to be adopted universally, blockchain systems must be scalable to support a high volume of transactions. As we increase the throughput of the DL system, the underlying peer-to-peer network might face multiple levels of challenges to keep up with the requirements. Due to varying network capacities, the slower nodes would be at a relative disadvantage compared to the faster ones, which could negatively impact their revenue. In order to quantify their relative advantage or disadvantage, we introduce two measures of network fairness, p_f, the probability of frontrunning and alpha_f, the publishing fairness. We show that as we scale the blockchain, both these measures deteriorate, implying that the slower nodes face a disadvantage at higher throughputs. It results in the faster nodes getting more than their fair share of the reward while the slower nodes (slow in terms of network quality) get less. Thus, fairness and scalability in blockchain systems do not go hand in hand.
In a setting with rational miners, lack of fairness causes miners to deviate from the “longest chain rule” or emph{undercut}, which would reduce the blockchain’s resilience against byzantine adversaries. Hence, fairness is not only a desirable property for a blockchain system but also essential for the security of the blockchain and any scalable blockchain protocol proposed must ensure fairness.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Blockchain-based Distributed Ledgers (DLs) promise to transform the existing financial system by making it truly democratic. In the past decade, blockchain technology has seen many novel applications ranging from the banking industry to real estate. However, in order to be adopted universally, blockchain systems must be scalable to support a high volume of transactions. As we increase the throughput of the DL system, the underlying peer-to-peer network might face multiple levels of challenges to keep up with the requirements. Due to varying network capacities, the slower nodes would be at a relative disadvantage compared to the faster ones, which could negatively impact their revenue. In order to quantify their relative advantage or disadvantage, we introduce two measures of network fairness, p_f, the probability of frontrunning and alpha_f, the publishing fairness. We show that as we scale the blockchain, both these measures deteriorate, implying that the slower nodes face a disadvantage at higher throughputs. It results in the faster nodes getting more than their fair share of the reward while the slower nodes (slow in terms of network quality) get less. Thus, fairness and scalability in blockchain systems do not go hand in hand.
In a setting with rational miners, lack of fairness causes miners to deviate from the “longest chain rule” or emph{undercut}, which would reduce the blockchain’s resilience against byzantine adversaries. Hence, fairness is not only a desirable property for a blockchain system but also essential for the security of the blockchain and any scalable blockchain protocol proposed must ensure fairness. |
| 80. | Ayush Deva; Kumar Abhishek; Sujit Gujar A Multi-Arm Bandit Approach To Subset Selection Under Constraints Conference International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021, 2021. BibTeX @conference{deva21,
title = {A Multi-Arm Bandit Approach To Subset Selection Under Constraints},
author = {Ayush Deva and Kumar Abhishek and Sujit Gujar},
year = {2021},
date = {2021-05-05},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 79. | Sankarshan Damle; Sujit Gujar; Moin Hussain Moti FASTEN: Fair and Secure Distributed Voting Using Smart Contracts Conference IEEE International Conference on Blockchain and Cryptocurrency (IEEE ICBC 2021), 2021. BibTeX @inproceedings{Damle2021,
title = {FASTEN: Fair and Secure Distributed Voting Using Smart Contracts},
author = {Sankarshan Damle and Sujit Gujar and Moin Hussain Moti},
year = {2021},
date = {2021-05-04},
booktitle = {IEEE International Conference on Blockchain and Cryptocurrency (IEEE ICBC 2021)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 78. | Ayush Deva; Kumar Abhishek; Sujit Gujar A Multi-Arm Bandit Approach To Subset Selection Under Constraints Workshop ALA 2021, Adaptive and Learning Agents Workshop at AAMAS 2021, 2021. BibTeX @workshop{Deva2021,
title = {A Multi-Arm Bandit Approach To Subset Selection Under Constraints},
author = {Ayush Deva and Kumar Abhishek and Sujit Gujar},
year = {2021},
date = {2021-05-03},
booktitle = {ALA 2021, Adaptive and Learning Agents Workshop at AAMAS 2021},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 77. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Narahari Y Ballooning Multi-armed Bandits Journal Article Forthcoming Journal of Artificial Intelligence, Forthcoming. BibTeX @article{Ghalme21a,
title = {Ballooning Multi-armed Bandits},
author = {Ganesh Ghalme and Swapnil Dhamal and Shweta Jain and Sujit Gujar and Y Narahari},
year = {2021},
date = {2021-04-05},
journal = {Journal of Artificial Intelligence},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
|
| 76. | Sankarshan Damle; Aleksei Triastcyn; Boi Faltings; Sujit Gujar Differentially Private Multi-Agent Constraint Optimization Workshop The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21), 2021. Abstract | Links | BibTeX @workshop{Damle2021b,
title = {Differentially Private Multi-Agent Constraint Optimization},
author = {Sankarshan Damle and Aleksei Triastcyn and Boi Faltings and Sujit Gujar},
editor = {Ferdinando Fioretto},
url = {https://ppai21.github.io/files/38-paper.pdf},
year = {2021},
date = {2021-02-08},
booktitle = {The Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21)},
abstract = {Several optimization scenarios involve multiple agents that
desire to protect the privacy of their preferences. There ex-
ist distributed algorithms for constraint optimization that pro-
vide improved privacy protection through secure multiparty
computation. However, it comes at the expense of high com-
putational complexity and does not constitute a rigorous pri-
vacy guarantee, as the result of the computation itself may
compromise agents’ preferences. In this work, we show how
to achieve differential privacy through randomization of the
solving process. In particular, we present P-Gibbs, which
adapts the SD-Gibbs algorithm to obtain differential privacy
guarantees with much higher computational efficiency. Ex-
periments on graph coloring and meeting scheduling show
the algorithm’s privacy-performance trade-off compared to
variants with uniform sampling and the SD-Gibbs algorithm.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Several optimization scenarios involve multiple agents that
desire to protect the privacy of their preferences. There ex-
ist distributed algorithms for constraint optimization that pro-
vide improved privacy protection through secure multiparty
computation. However, it comes at the expense of high com-
putational complexity and does not constitute a rigorous pri-
vacy guarantee, as the result of the computation itself may
compromise agents’ preferences. In this work, we show how
to achieve differential privacy through randomization of the
solving process. In particular, we present P-Gibbs, which
adapts the SD-Gibbs algorithm to obtain differential privacy
guarantees with much higher computational efficiency. Ex-
periments on graph coloring and meeting scheduling show
the algorithm’s privacy-performance trade-off compared to
variants with uniform sampling and the SD-Gibbs algorithm. |
| 75. | Maanik Arora; Naresh Manwani Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels Conference Forthcoming CoDS-COMAD 2021, Forthcoming. BibTeX @conference{Maanik2021_CODS-COMAD,
title = {Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels},
author = {Maanik Arora and Naresh Manwani},
year = {2021},
date = {2021-01-10},
booktitle = {CoDS-COMAD 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
2020
|
| 74. | Sanidhay Arora; Anurag Jain; Sankarshan Damle; Sujit Gujar ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol Workshop Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020), 2020. BibTeX @workshop{Arora2020,
title = {ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol},
author = {Sanidhay Arora and Anurag Jain and Sankarshan Damle and Sujit Gujar},
editor = {Jing Chen and Xiaotie Deng},
year = {2020},
date = {2020-12-11},
booktitle = {Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020)},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 73. | Anurag Jain; Sujit Gujar Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols Workshop Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020), 2020. BibTeX @workshop{Jain2020,
title = {Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols},
author = {Anurag Jain and Sujit Gujar },
editor = {Jing Chen and Xiaotie Deng},
year = {2020},
date = {2020-12-11},
booktitle = {Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020)},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 72. | Maanik Arora; Naresh Manwani Exact Passive Aggressive Algorithm for Multiclass Classification Using Bandit Feedbacks Conference ACML 2020, 2020. Links | BibTeX @conference{Maanik2020_ACML,
title = {Exact Passive Aggressive Algorithm for Multiclass Classification Using Bandit Feedbacks},
author = {Maanik Arora and Naresh Manwani},
url = {http://proceedings.mlr.press/v129/arora20a.html},
year = {2020},
date = {2020-11-30},
booktitle = {ACML 2020},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 71. | Bhanu Garg; Naresh Manwani Robust Deep Ordinal Regression under Label Noise Conference ACML 2020, 2020. Links | BibTeX @conference{Bhanu2020_ACML,
title = {Robust Deep Ordinal Regression under Label Noise},
author = {Bhanu Garg and Naresh Manwani},
url = {http://proceedings.mlr.press/v129/garg20a.html},
year = {2020},
date = {2020-11-30},
booktitle = {ACML 2020},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 70. | 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. |
| 69. | 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}
}
|
| 68. | 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}
}
|
| 67. | 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}
}
|
| 66. | 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}
}
|
| 65. | 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}
}
|
| 64. | 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}
}
|
| 63. | 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}
}
|
| 62. | 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}
}
|
| 61. | 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}
}
|
| 60. | 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}
}
|
| 59. | 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}
}
|
| 58. | 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}
}
|
| 57. | 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}
}
|
| 56. | 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. |
| 55. | 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. |
| 54. | 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}
}
|
| 53. | 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}
}
|
| 52. | 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}
}
|
| 51. | 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}
}
|
| 50. | 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}
}
|
| 49. | 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}
}
|
| 48. | 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. |
| 47. | 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
|
| 46. | 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}
}
|
| 45. | 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}
}
|
| 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}
}
|
