2022
|
| 118. | Sambhav Solanki; Samhita Kanaparthy; Sankarshan Damle; Sujit Gujar Differentially Private Federated Combinatorial Bandits with Constraints Conference In the proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’22), 2022. BibTeX @conference{Sambhav22,
title = {Differentially Private Federated Combinatorial Bandits with Constraints},
author = {Sambhav Solanki and Samhita Kanaparthy and Sankarshan Damle and Sujit Gujar},
year = {2022},
date = {2022-09-19},
booktitle = {In the proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’22)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 117. | Reza Hadi Mogavi; Ehsan-Ul Haq; Sujit Gujar; Pan Hui; Xiaojuan Ma More Gamification Is Not Always Better: A Case Study of Promotional Gamification in a Question Answering Website Conference In the proceedings of 25th ACM Conference on Computer Supported Cooperative Work (CSCW’22), 2022. BibTeX @conference{reza2022,
title = {More Gamification Is Not Always Better: A Case Study of Promotional Gamification in a Question Answering Website},
author = {Reza Hadi Mogavi and Ehsan-Ul Haq and Sujit Gujar and Pan Hui and Xiaojuan Ma},
year = {2022},
date = {2022-08-01},
booktitle = {In the proceedings of 25th ACM Conference on Computer Supported Cooperative Work (CSCW’22)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 116. | Shantanu Das; Swapnil Dhamal; Ganesh Ghalme; Shweta Jain; Sujit Gujar Individual Fairness in Feature-Based Pricing for Monopoly Markets Conference In the proceedings of 38th Conference on Uncertainty in Artificial Intelligence (UAI’22), 2022. BibTeX @conference{shantanu22UAI,
title = {Individual Fairness in Feature-Based Pricing for Monopoly Markets},
author = {Shantanu Das and Swapnil Dhamal and Ganesh Ghalme and Shweta Jain and Sujit Gujar},
year = {2022},
date = {2022-08-01},
booktitle = {In the proceedings of 38th Conference on Uncertainty in Artificial Intelligence (UAI’22)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 115. | Sanjay Chandlekar; Bala Suraj Pedasingu; Easwar Subramanian; Sanjay Bhat P; Sujit Gujar An Autonomous Intelligent Broker for Smart-grids Conference In the proceedings of the 31st International Joint Conference of Artificial Intelligence (IJCAI’22) , 2022. BibTeX @conference{sanjay22ijcai,
title = {An Autonomous Intelligent Broker for Smart-grids},
author = {Sanjay Chandlekar and Bala Suraj Pedasingu and Easwar Subramanian and Sanjay P Bhat and Sujit Gujar},
year = {2022},
date = {2022-07-23},
booktitle = { In the proceedings of the 31st International Joint Conference of Artificial Intelligence (IJCAI’22) },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 114. | Narendra Babu Unnam; Krishna Reddy P; Amit Pandey; Naresh Manwani Journey to the center of the words: Word weighting scheme based on the geometry of word embeddings Conference Forthcoming 34th International Conference on Scientific and Statistical Database Management (SSDBM), Forthcoming. BibTeX @conference{Narendra2022,
title = {Journey to the center of the words: Word weighting scheme based on the geometry of word embeddings},
author = {Narendra Babu Unnam and P Krishna Reddy and Amit Pandey and Naresh Manwani},
year = {2022},
date = {2022-07-08},
booktitle = {34th International Conference on Scientific and Statistical Database Management (SSDBM)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 113. | Manika Sharma; Kishore Kothapalli; Sujit Gujar BLOCKVAC: A Universally Acceptable and Ideal Vaccination System on Blockchain Conference 5th IEEE International Conference on Blockchain, 2022. BibTeX @conference{manika22,
title = {BLOCKVAC: A Universally Acceptable and Ideal Vaccination System on Blockchain},
author = {Manika Sharma and Kishore Kothapalli and Sujit Gujar},
year = {2022},
date = {2022-06-25},
booktitle = {5th IEEE International Conference on Blockchain},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 112. | Mudit Agarwal; Naresh Manwani ALBIF: Active Learning with BandIt Feedbacks Conference 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2022. BibTeX @conference{Mudit2022,
title = {ALBIF: Active Learning with BandIt Feedbacks},
author = {Mudit Agarwal and Naresh Manwani},
year = {2022},
date = {2022-05-19},
booktitle = {26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 111. | Sanjay Chandlekar; Easwar Subramanian; Sanjay Bhat P; Sujit Gujar Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids Conference Forthcoming Proceedings of International Conference on Autonomous Agents and Multiagent Systems, AAMAS’22, Forthcoming. BibTeX @conference{Sanjay22addpg,
title = {Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids},
author = {Sanjay Chandlekar and Easwar Subramanian and Sanjay P Bhat and Sujit Gujar},
year = {2022},
date = {2022-05-09},
booktitle = {Proceedings of International Conference on Autonomous Agents and Multiagent Systems, AAMAS’22},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 110. | Samhita Kanaparthy; Sankarshan Damle; Sujit Gujar REFORM: Reputation Based Fair and Temporal Reward Framework for Crowdsourcing Conference In the Proceedings of International Conference on Autonomous Agents and Multiagent Systems, AAMAS’22, 2022. BibTeX @conference{Kanaparthy22reform,
title = {REFORM: Reputation Based Fair and Temporal Reward Framework for Crowdsourcing},
author = {Samhita Kanaparthy and Sankarshan Damle and Sujit Gujar},
year = {2022},
date = {2022-05-09},
booktitle = {In the Proceedings of International Conference on Autonomous Agents and Multiagent Systems, AAMAS’22},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 109. | Debojit Das; Shweta Jain; Sujit Gujar Budgeted Combinatorial Multi-Armed Bandits Conference In the Proceedings of International Conference on Autonomous Agents and Multiagent Systems, 2022. BibTeX @conference{debojit22bcmab,
title = {Budgeted Combinatorial Multi-Armed Bandits},
author = {Debojit Das and Shweta Jain and Sujit Gujar },
year = {2022},
date = {2022-05-09},
booktitle = {In the Proceedings of International Conference on Autonomous Agents and Multiagent Systems},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 108. | Sankarshan Damle; Padala Manisha; Sujit Gujar Welfare Optimal Combinatorial Civic Crowdfunding with Budgeted Agents Workshop Games, Agents and Incentives Workshop (GAIW’22)@AAMAS’22, 2022. BibTeX @workshop{sankarshan2022a,
title = {Welfare Optimal Combinatorial Civic Crowdfunding with Budgeted Agents},
author = {Sankarshan Damle and Padala Manisha and Sujit Gujar},
year = {2022},
date = {2022-05-09},
booktitle = {Games, Agents and Incentives Workshop (GAIW’22)@AAMAS’22},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 107. | Anurag Jain; Sanidhay Arora; Sankarshan Damle; Sujit Gujar Tiramisu: Layering Consensus Protocols for Scalable and Secure Blockchains Conference Forthcoming IEEE International Conference on Blockchain and Cryptocurrency, Forthcoming. BibTeX @conference{Anurag22a,
title = {Tiramisu: Layering Consensus Protocols for Scalable and Secure Blockchains},
author = {Anurag Jain and Sanidhay Arora and Sankarshan Damle and Sujit Gujar },
year = {2022},
date = {2022-05-03},
booktitle = {IEEE International Conference on Blockchain and Cryptocurrency},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 106. | Kritika Prakash; Fiza Hussain; Praveen Paruchuri; Sujit Gujar How Private Is Your RL Policy? An Inverse RL Based Analysis Framework Conference Forthcoming 36th AAAI Conference on Artificial Intelligence , AAAI, Forthcoming. BibTeX @inproceedings{Kritika22,
title = {How Private Is Your RL Policy? An Inverse RL Based Analysis Framework},
author = {Kritika Prakash and Fiza Hussain and Praveen Paruchuri and Sujit Gujar},
year = {2022},
date = {2022-02-22},
booktitle = {36th AAAI Conference on Artificial Intelligence },
publisher = {AAAI},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 105. | Sreenivasan Mohandas; Naresh Manwani; Durga Prasad Dhulipudi Momentum Iterative Gradient Sign Method outperforms PGD Attacks Conference 14th International Conference on Agents and Artificial Intelligence (ICAART), 2022. BibTeX @conference{Srenivasan2022,
title = {Momentum Iterative Gradient Sign Method outperforms PGD Attacks},
author = {Sreenivasan Mohandas and Naresh Manwani and Durga Prasad Dhulipudi},
year = {2022},
date = {2022-02-05},
booktitle = {14th International Conference on Agents and Artificial Intelligence (ICAART)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 104. | Samhita Kanaparthy; Manisha Padala; Sankarshan Damle; Sujit Gujar Fair Federated Learning for Heterogeneous Data Conference Young Researchers Symposium, CODS-COMAD’22., 2022. BibTeX @conference{Samhita22a,
title = {Fair Federated Learning for Heterogeneous Data},
author = {Samhita Kanaparthy and Manisha Padala and Sankarshan Damle and Sujit Gujar},
year = {2022},
date = {2022-01-05},
booktitle = {Young Researchers Symposium, CODS-COMAD’22.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 103. | Ashutosh Ranjan; Vinay Ramani; Sujit Gujar Dynamic Matching in Campus Placements: The Benefits and Affordability of the Dream Option Journal Article Forthcoming IMB Management Review, Forthcoming. BibTeX @article{Ashutosh22,
title = {Dynamic Matching in Campus Placements: The Benefits and Affordability of the Dream Option},
author = {Ashutosh Ranjan and Vinay Ramani and Sujit Gujar},
year = {2022},
date = {2022-01-01},
journal = {IMB Management Review},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
|
2021
|
| 102. | Sankarshan Damle; Aleksei Triastcyn; Boi Faltings; Sujit Gujar Differentially Private Multi-Agent Constraint Optimization Conference Forthcoming To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’21), Forthcoming. BibTeX @conference{Damle2021c,
title = {Differentially Private Multi-Agent Constraint Optimization},
author = {Sankarshan Damle and Aleksei Triastcyn and Boi Faltings and Sujit Gujar},
year = {2021},
date = {2021-12-14},
booktitle = {To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’21)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 101. | Sankarshan Damle; Boi Faltings; Sujit Gujar Blockchain-based Practical Multi-agent Secure Comparison and its Application in Auctions Conference Forthcoming To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’21), Forthcoming. BibTeX @inproceedings{Damle2021d,
title = {Blockchain-based Practical Multi-agent Secure Comparison and its Application in Auctions},
author = {Sankarshan Damle and Boi Faltings and Sujit Gujar},
year = {2021},
date = {2021-12-14},
booktitle = {To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’21)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 100. | Manisha Padala; Sankarshan Damle; Sujit Gujar Learning Equilibrium Contributions in Multi-project Civic Crowdfunding Conference Forthcoming To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’21), Forthcoming. Links | BibTeX @conference{Padala2021b,
title = {Learning Equilibrium Contributions in Multi-project Civic Crowdfunding},
author = {Manisha Padala and Sankarshan Damle and Sujit Gujar},
url = {http://mll.iiit.ac.in/wp-content/uploads/2022/04/wi_iat_camera_ready_compressed-2.pdf},
year = {2021},
date = {2021-12-14},
booktitle = {To Appear in the Proceeding of The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’21)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 99. | Manisha Padala; Sujit Gujar Mechanism Design without Money for Fair Allocations Conference Forthcoming To Appear in the proceedings of the 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 2021 (WI-IAT ’21)., Forthcoming. BibTeX @conference{Padala2021c,
title = {Mechanism Design without Money for Fair Allocations},
author = {Manisha Padala and Sujit Gujar},
year = {2021},
date = {2021-12-14},
booktitle = {To Appear in the proceedings of the 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 2021 (WI-IAT ’21).},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 98. | Manisha Padala; Debojit Das; Sujit Gujar Effect of Input Noise Dimension in GANs Conference Forthcoming Proceedings 28th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society 2021 (ICONIP ’21), Forthcoming. BibTeX @inproceedings{Padala2021,
title = {Effect of Input Noise Dimension in GANs},
author = {Manisha Padala and Debojit Das and Sujit Gujar
},
year = {2021},
date = {2021-12-08},
booktitle = {Proceedings 28th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society 2021 (ICONIP ’21)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 97. | Manisha Padala; Sankarshan Damle ; Sujit Gujar Federated Learning Meets Fairness and Differential Privacy Conference Forthcoming Proceedings of the 28th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society 2021 (ICONIP ’21), Forthcoming. BibTeX @inproceedings{Manisha2021b,
title = {Federated Learning Meets Fairness and Differential Privacy},
author = {Manisha Padala and Sankarshan Damle, and Sujit Gujar},
year = {2021},
date = {2021-12-08},
publisher = {Proceedings of the 28th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific Neural Network Society 2021 (ICONIP ’21)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
|
| 96. | Gaurav Batra; Naresh Manwani Multiclass Classification using Dilute Bandit Feedback Conference Forthcoming PRICAI 2021, Forthcoming. BibTeX @conference{Gaurav2021,
title = {Multiclass Classification using Dilute Bandit Feedback},
author = {Gaurav Batra and Naresh Manwani},
year = {2021},
date = {2021-11-12},
booktitle = {PRICAI 2021},
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 95. | Meghana Reddy; Akansha Singh; Shweta Jain; Sujit Gujar Designing Bounded Min-knapsack Bandits Algorithm for Sustainable Demand Response Conference Forthcoming 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021) , Forthcoming. BibTeX @conference{MReddy2021b,
title = {Designing Bounded Min-knapsack Bandits Algorithm for Sustainable Demand Response},
author = {Meghana Reddy and Akansha Singh and Shweta Jain and Sujit Gujar},
year = {2021},
date = {2021-11-08},
booktitle = {18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021) },
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 94. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding Conference Forthcoming 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021) , Forthcoming. BibTeX @conference{sankarshan2021b,
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},
year = {2021},
date = {2021-11-08},
booktitle = {18th Pacific Rim International Conference on Artificial Intelligence (PRICAI-2021) },
keywords = {},
pubstate = {forthcoming},
tppubtype = {conference}
}
|
| 93. | 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}
}
|
| 92. | 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}
}
|
| 91. | 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}
}
|
| 90. | Meghana Reddy; Akansha Singh; Zoltan Nagy; Sujit Gujar; Shweta Jain Designing Bounded min-Knapsack Bandits Algorithm for Sustainable Demand Response Workshop Tackling Climate Change with Machine Learning, ICML , 2021. BibTeX @workshop{MReddy2021a,
title = {Designing Bounded min-Knapsack Bandits Algorithm for Sustainable Demand Response},
author = {Meghana Reddy and Akansha Singh and Zoltan Nagy and Sujit Gujar and Shweta Jain},
year = {2021},
date = {2021-07-23},
booktitle = {Tackling Climate Change with Machine Learning, ICML },
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
| 89. | Padala Manisha; Sankarshan Damle; Sujit Gujar Building Ethical AI: Federated Learning meets Fairness and Privacy Conference First Indian Conference on Deployable AI, 2021. BibTeX @conference{manisha2021a,
title = {Building Ethical AI: Federated Learning meets Fairness and Privacy},
author = {Padala Manisha and Sankarshan Damle and Sujit Gujar },
year = {2021},
date = {2021-06-17},
booktitle = {First Indian Conference on Deployable AI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
| 88. | 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}
}
|
| 87. | 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}
}
|
| 86. | 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. |
| 85. | 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}
}
|
| 84. | 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}
}
|
| 83. | 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}
}
|
| 82. | 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}
}
|
| 81. | 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. |
| 80. | 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}
}
|
| 79. | Daksh Anand; Vaibhav Gupta; Praveen Paruchuri; Balaraman Ravindran An Enhanced Advising Model in Teacher-Student Framework using State
Categorization Conference Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9,
2021, pp. 6653–6660, AAAI Press, 2021. Links | BibTeX @inproceedings{DBLP:conf/aaai/AnandGPR21,
title = {An Enhanced Advising Model in Teacher-Student Framework using State
Categorization},
author = {Daksh Anand and Vaibhav Gupta and Praveen Paruchuri and Balaraman Ravindran},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/16823},
year = {2021},
date = {2021-01-01},
booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9,
2021},
pages = {6653–6660},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 78. | Vaibhav Gupta; Daksh Anand; Praveen Paruchuri; Akshat Kumar Action Selection for Composable Modular Deep Reinforcement Learning Conference Dignum, Frank; Lomuscio, Alessio; Endriss, Ulle; é, Ann Now (Ed.): AAMAS ’21: 20th International Conference on Autonomous Agents and
Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021, pp. 565–573, ACM, 2021. Links | BibTeX @inproceedings{DBLP:conf/atal/GuptaAPK21,
title = {Action Selection for Composable Modular Deep Reinforcement Learning},
author = {Vaibhav Gupta and Daksh Anand and Praveen Paruchuri and Akshat Kumar},
editor = {Frank Dignum and Alessio Lomuscio and Ulle Endriss and Ann Now \'{e}},
url = {https://dl.acm.org/doi/10.5555/3463952.3464022},
year = {2021},
date = {2021-01-01},
booktitle = {AAMAS ’21: 20th International Conference on Autonomous Agents and
Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021},
pages = {565–573},
publisher = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 77. | Yash Chaurasia; Visvesh Subramanian; Sujit Gujar PUPoW: A Framework for Designing Blockchains with Practically-Useful-Proof-of-Work & VanityCoin Conference 2021 IEEE International Conference on Blockchain (Blockchain), pp. 122–129, IEEE 2021. BibTeX @inproceedings{chaurasia2021pupow,
title = {PUPoW: A Framework for Designing Blockchains with Practically-Useful-Proof-of-Work & VanityCoin},
author = {Yash Chaurasia and Visvesh Subramanian and Sujit Gujar},
year = {2021},
date = {2021-01-01},
booktitle = {2021 IEEE International Conference on Blockchain (Blockchain)},
pages = {122–129},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
| 76. | Dimitris Chatzopoulos; Anurag Jain; Sujit Gujar; Boi Faltings; Pan Hui Towards Mobile Distributed Ledgers Journal Article IEEE Internet of Things Journal, 2021. BibTeX @article{chatzopoulos2021mobile,
title = {Towards Mobile Distributed Ledgers},
author = {Dimitris Chatzopoulos and Anurag Jain and Sujit Gujar and Boi Faltings and Pan Hui},
year = {2021},
date = {2021-01-01},
journal = {IEEE Internet of Things Journal},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2020
|
| 75. | 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}
}
|
| 74. | 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}
}
|
| 73. | 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}
}
|
| 72. | 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}
}
|
| 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}
}
|
