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Found 19 papers, 1 papers with code
Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications
Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied.
An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction
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.
Maxmin Participatory Budgeting
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.
Ballooning Multi-Armed Bandits
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model.
Achieving Fairness in the Stochastic Multi-armed Bandit Problem
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
Achieving Fairness in Stochastic Multi-armed Bandit Problem
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
Groupwise Maximin Fair Allocation of Indivisible Goods
We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations.
A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading
We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem.
An Iterative Transfer Learning Based Ensemble Technique for Automatic Short Answer Grading
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.
Complexity of Manipulation with Partial Information in Voting
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.
Topic Model Based Multi-Label Classification from the Crowd
Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes.
A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing
The time to failure of a worker depends on the duration of the task handled by the worker.
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds
We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint.
On Choosing Committees Based on Approval Votes in the Presence of Outliers
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).
Estimating the Margin of Victory of an Election using Sampling
The margin of victory of an election is a useful measure to capture the robustness of an election outcome.
Manipulation is Harder with Incomplete Votes
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.
Frugal Bribery in Voting
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.
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
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).
