Search Publications

@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 = {Bandit Feedback, Label Noise, multiclass classification, online learning},
pubstate = {forthcoming},
tppubtype = {conference}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}

@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}
}