no code implementations • 11 Jan 2024 • V. Udaya Sankar, Vishisht Srihari Rao, Y. Narahari
Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied.
no code implementations • 15 Apr 2023 • Mayank Ratan Bhardwaj, Jaydeep Pawar, Abhijnya Bhat, Deepanshu, Inavamsi Enaganti, Kartik Sagar, Y. Narahari
In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices.
1 code implementation • 29 Apr 2022 • Gogulapati Sreedurga, Mayank Ratan Bhardwaj, Y. Narahari
Participatory Budgeting (PB) is a popular voting method by which a limited budget is divided among a set of projects, based on the preferences of voters over the projects.
no code implementations • 24 Jan 2020 • Ganesh Ghalme, Swapnil Dhamal, Shweta Jain, Sujit Gujar, Y. Narahari
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model.
no code implementations • 23 Jul 2019 • Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
no code implementations • 27 May 2019 • Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
no code implementations • 21 Nov 2017 • Siddharth Barman, Arpita Biswas, Sanath Kumar Krishnamurthy, Y. Narahari
We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations.
no code implementations • WS 2016 • Shourya Roy, D, S apat, ipan, Y. Narahari
We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem.
no code implementations • 16 Sep 2016 • Shourya Roy, Himanshu S. Bhatt, Y. Narahari
We propose an iterative technique on an ensemble of (a) a text classifier of student answers and (b) a classifier using numeric features derived from various similarity measures with respect to model answers.
no code implementations • 15 Apr 2016 • Palash Dey, Neeldhara Misra, Y. Narahari
Opportunistic Manipulation (OM): the manipulators seek to vote in a way that makes their preferred candidate win in every viable extension of the partial votes of the non-manipulators.
no code implementations • 4 Apr 2016 • Divya Padmanabhan, Satyanath Bhat, Shirish Shevade, Y. Narahari
Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes.
no code implementations • 12 Feb 2016 • Satyanath Bhat, Divya Padmanabhan, Shweta Jain, Y. Narahari
The time to failure of a worker depends on the duration of the task handled by the worker.
no code implementations • 25 Jan 2016 • Divya Padmanabhan, Satyanath Bhat, Dinesh Garg, Shirish Shevade, Y. Narahari
We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint.
no code implementations • 13 Nov 2015 • Palash Dey, Neeldhara Misra, Y. Narahari
For net approval and minisum approval voting rules, we provide a dichotomous result, resolving the parameterized complexity of this problem for all subsets of five natural parameters considered (by showing either FPT or W[1]-hardness for all subsets of parameters).
no code implementations • 4 May 2015 • Palash Dey, Y. Narahari
The margin of victory of an election is a useful measure to capture the robustness of an election outcome.
no code implementations • 30 Apr 2015 • Palash Dey, Neeldhara Misra, Y. Narahari
The CM problem, however, has been studied only in the complete information setting, that is, when the manipulators know the votes of the non-manipulators.
no code implementations • 30 Apr 2015 • Palash Dey, Neeldhara Misra, Y. Narahari
However, the Frugal-{dollar}bribery problem is intractable for all the voting rules studied here barring the plurality and the veto voting rules for unweighted elections.
no code implementations • 27 Jun 2014 • Shweta Jain, Sujit Gujar, Satyanath Bhat, Onno Zoeter, Y. Narahari
First, we propose a framework, Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained Confidence Bound for a Non Strategic setting (CCB-NS).