1. | Anurag Jain; Shoeb Siddiqui; Sujit Gujar: We might walk together, but I run faster: Network Fairness and Scalability in Blockchains. Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Forthcoming. (Type: Conference | Abstract | BibTeX | Tags: Blockchains, Distributed Ledgers, fairness, Peer-to-Peer Networks, Scalability) @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 = {Blockchains, Distributed Ledgers, fairness, Peer-to-Peer Networks, Scalability}, pubstate = {forthcoming}, 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. |
2. | Ayush Deva; Kumar Abhishek; Sujit Gujar: A Multi-Arm Bandit Approach To Subset Selection Under Constraints. International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021, Forthcoming. (Type: Conference | BibTeX | Tags: auctions, Mechanism Design, Multi-arm Bandits) @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 = {auctions, Mechanism Design, Multi-arm Bandits}, pubstate = {forthcoming}, tppubtype = {conference} } |
3. | Maanik Arora; Naresh Manwani: Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels. CoDS-COMAD 2021, Forthcoming. (Type: Conference | BibTeX | Tags: multiclass classification, online learning) @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 = {multiclass classification, online learning}, pubstate = {forthcoming}, tppubtype = {conference} } |
4. | Sanidhay Arora; Anurag Jain; Sankarshan Damle; Sujit Gujar: ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol. Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020), 2020. (Type: Workshop | BibTeX | Tags: Blockchain, Blockchain Consensus Protocols, Game Theory, Scalable Blockchain) @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 = {Blockchain, Blockchain Consensus Protocols, Game Theory, Scalable Blockchain}, pubstate = {published}, tppubtype = {workshop} } |
5. | Anurag Jain; Sujit Gujar: Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols. Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020), 2020. (Type: Workshop | BibTeX | Tags: Block Rewards, Blockchains, Distributed Ledgers, Faithful Implementation, Game Theory) @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 = {Block Rewards, Blockchains, Distributed Ledgers, Faithful Implementation, Game Theory}, pubstate = {published}, tppubtype = {workshop} } |
6. | Maanik Arora; Naresh Manwani: Exact Passive Aggressive Algorithm for Multiclass Classification Using Bandit Feedbacks. ACML 2020, 2020. (Type: Conference | Links | BibTeX | Tags: ) @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} } |
7. | Bhanu Garg; Naresh Manwani: Robust Deep Ordinal Regression under Label Noise. ACML 2020, 2020. (Type: Conference | Links | BibTeX | Tags: Label Noise, Ordinal Regression) @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 = {Label Noise, Ordinal Regression}, pubstate = {published}, tppubtype = {conference} } |
8. | Padala Manisha; Sujit Gujar: FNNC: Achieving Fairness through Neural Networks. The 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, 2020. (Type: Conference | Abstract | BibTeX | Tags: fairness, Neural Networks) @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 = {fairness, Neural Networks}, 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. |
9. | Praveen Paruchuri; Sreeja Kamishetty; Soumya Vadlamannati: System and method for controlling vehicular pollution concentration and providing maximum traffic flow throughput . 2020. (Type: Patent | BibTeX | Tags: patent) @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 = {patent}, pubstate = {published}, tppubtype = {patent} } |
10. | Rajarshi Bhattacharjee; Naresh Manwani: Online Algorithms for Multiclass Classification using Partial Labels. In: PAKDD 2020, 2020. (Type: Conference | Links | BibTeX | Tags: multiclass classification, online learning, partial label) @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 = {multiclass classification, online learning, partial label}, pubstate = {published}, tppubtype = {inproceedings} } |
11. | Shoeb Siddiqui; Sujit Gujar; Ganesh Vanahalli: BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model. International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. (Type: Conference | BibTeX | Tags: bitcoin, fairness) @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 = {bitcoin, fairness}, pubstate = {published}, tppubtype = {conference} } |
12. | Kumar Abhishek; Shweta Jain; Sujit Gujar: Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions. International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, 2020. (Type: Conference | BibTeX | Tags: auctions, online learning) @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 = {auctions, online learning}, pubstate = {published}, tppubtype = {conference} } |
13. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Yadati Narahari: Ballooning Multi-Armed Bandits. International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. (Type: Conference | BibTeX | Tags: ActiveLearning, MAB, online learning) @conference{ghalme20a, title = {Ballooning Multi-Armed Bandits}, author = {Ganesh Ghalme and Swapnil Dhamal and Shweta Jain and Sujit Gujar and Yadati Narahari}, year = {2020}, date = {2020-05-11}, booktitle = {International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020}, keywords = {ActiveLearning, MAB, online learning}, pubstate = {published}, tppubtype = {conference} } |
14. | Sankarshan Damle; Moin Hussain Moti; Praphul Chandra; Sujit Gujar: Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding. Games, Agents, and Incentives Workshop (GAIW@AAMAS 2020), 2020. (Type: Workshop | Links | BibTeX | Tags: Blockchains, Civic Crowdfunding, Ethereum) @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 = {Blockchains, Civic Crowdfunding, Ethereum}, pubstate = {published}, tppubtype = {workshop} } |
15. | Moin Hussain Moti; Dimitris Chatzopoulos; Pan Hui; Boi Faltings; Sujit Gujar: Orthos: A Trustworthy AI Framework For Data Acquisition. 8th International Workshop on Engineering Multi-Agent Systems (EMAS 2020), 2020. (Type: Workshop | BibTeX | Tags: Blockchains, Crowdsensing) @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 = {Blockchains, Crowdsensing}, pubstate = {published}, tppubtype = {workshop} } |
16. | Ganesh Ghalme; Swapnil Dhamal; Shweta Jain; Sujit Gujar; Narahari Y: Ballooning Multi-Armed Bandits. Adaptive and Learning Agents, 2020. (Type: Workshop | BibTeX | Tags: MAB) @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 = {MAB}, pubstate = {published}, tppubtype = {workshop} } |
17. | Dimitris Chatzopoulos; Sujit Gujar; Boi Faltings; Pan Hui: Mneme: A Mobile Distributed Ledger. In: IEEE Conference on Communication, INFOCOM, IEEE, 2020. (Type: Conference | BibTeX | Tags: ) @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} } |
18. | Naresh Manwani; Mohit Chandra: Exact Passive Aggressive Learning of Ordinal Regression Using Interval Labels. In: IEEE Transactions on Neural Networks and Learning Systems (NNLS), 2020. (Type: Journal Article | Links | BibTeX | Tags: Online, Ordinal Regression, Passive-Aggressive) @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 = {Online, Ordinal Regression, Passive-Aggressive}, pubstate = {published}, tppubtype = {article} } |
19. | Susobhan Ghosh; Sujit Gujar; Praveen Paruchuri; Easwar Subramanian; Sanjay P: Bidding in Smart Grid PDAs: Theory, Analysis and Strategy. In: AAAI, AAAI Press, 2020. (Type: Conference | Links | BibTeX | Tags: auctions, bidding, equilibrium, Game Theory) @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 = {auctions, bidding, equilibrium, Game Theory}, pubstate = {published}, tppubtype = {inproceedings} } |
20. | Shweta Jain; Sujit Gujar: A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids. In: AAAI, AAAI Press, 2020. (Type: Conference | BibTeX | Tags: ) @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} } |
1. | We might walk together, but I run faster: Network Fairness and Scalability in Blockchains. Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), Forthcoming. | :
2. | A Multi-Arm Bandit Approach To Subset Selection Under Constraints. International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021, Forthcoming. | :
3. | Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels. CoDS-COMAD 2021, Forthcoming. | :
4. | ASHWAChain: A Fast, Scalable and Strategy-proof Committee-based Blockchain Protocol. Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020), 2020. | :
5. | Block Rewards, Not Transaction Fees Keep Miners Faithful In Blockchain Protocols. Workshop on Game Theory in Blockchain at WINE 2020 (GTiB@WINE 2020), 2020. | :
6. | Exact Passive Aggressive Algorithm for Multiclass Classification Using Bandit Feedbacks. ACML 2020, 2020. | :
7. | Robust Deep Ordinal Regression under Label Noise. ACML 2020, 2020. | :
8. | FNNC: Achieving Fairness through Neural Networks. The 29th International Joint Conference on Artificial Intelligence, IJCAI 2020, 2020. | :
9. | System and method for controlling vehicular pollution concentration and providing maximum traffic flow throughput . 2020. | :
10. | Online Algorithms for Multiclass Classification using Partial Labels. In: PAKDD 2020, 2020. | :
11. | BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model. International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. | :
12. | Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions. International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, 2020. | :
13. | Ballooning Multi-Armed Bandits. International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2020, 2020. | :
14. | Designing Refund Bonus Schemes for Provision Point Mechanism in Civic Crowdfunding. Games, Agents, and Incentives Workshop (GAIW@AAMAS 2020), 2020. | :
15. | Orthos: A Trustworthy AI Framework For Data Acquisition. 8th International Workshop on Engineering Multi-Agent Systems (EMAS 2020), 2020. | :
16. | Ballooning Multi-Armed Bandits. Adaptive and Learning Agents, 2020. | :
17. | Mneme: A Mobile Distributed Ledger. In: IEEE Conference on Communication, INFOCOM, IEEE, 2020. | :
18. | Exact Passive Aggressive Learning of Ordinal Regression Using Interval Labels. In: IEEE Transactions on Neural Networks and Learning Systems (NNLS), 2020. | :
19. | Bidding in Smart Grid PDAs: Theory, Analysis and Strategy. In: AAAI, AAAI Press, 2020. | :
20. | A Multiarmed Bandit Based Incentive Mechanism for a Subset Selection of Customers for Demand Response in Smart Grids. In: AAAI, AAAI Press, 2020. | :