Search Results for author: Arpita Biswas

Found 15 papers, 4 papers with code

Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks

no code implementations1 Mar 2023 Arpita Biswas, Jackson A. Killian, Paula Rodriguez Diaz, Susobhan Ghosh, Milind Tambe

The goal is to plan an intervention schedule that maximizes the expected reward while satisfying budget constraints on each worker as well as fairness in terms of the load assigned to each worker.

Fairness Multi-Armed Bandits +1

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

1 code implementation11 May 2022 Lily Xu, Arpita Biswas, Fei Fang, Milind Tambe

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land.

Towards Fair Recommendation in Two-Sided Platforms

1 code implementation26 Dec 2021 Arpita Biswas, Gourab K Patro, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services.

Fairness Vocal Bursts Valence Prediction

Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning

no code implementations4 Jul 2021 Jackson A. Killian, Lily Xu, Arpita Biswas, Milind Tambe

Our approach uses a double oracle framework (oracles for \textit{agent} and \textit{nature}), which is often used for single-process robust planning but requires significant new techniques to accommodate the combinatorial nature of RMABs.

Multi-agent Reinforcement Learning Multi-Armed Bandits +1

Q-Learning Lagrange Policies for Multi-Action Restless Bandits

1 code implementation22 Jun 2021 Jackson A. Killian, Arpita Biswas, Sanket Shah, Milind Tambe

Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which $N$ independent processes are managed.

Multi-Armed Bandits Q-Learning

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

no code implementations17 May 2021 Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.

Q-Learning

Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems

no code implementations8 Mar 2021 Aditya Mate, Arpita Biswas, Christoph Siebenbrunner, Susobhan Ghosh, Milind Tambe

Our contributions are as follows: (1) We derive conditions under which our problem satisfies indexability, a precondition that guarantees the existence and asymptotic optimality of the Whittle Index solution for RMABs.

Multi-Armed Bandits

Ensuring Fairness under Prior Probability Shifts

no code implementations6 May 2020 Arpita Biswas, Suvam Mukherjee

CAPE makes novel use of prevalence estimation techniques, sampling and an ensemble of classifiers to ensure fair predictions under prior probability shifts.

Fairness

COVID-19: Strategies for Allocation of Test Kits

no code implementations3 Apr 2020 Arpita Biswas, Shruthi Bannur, Prateek Jain, Srujana Merugu

Thus, it is important to allocate a separate budget of test-kits per day targeted towards preventing community spread and detecting new cases early on.

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

2 code implementations25 Feb 2020 Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.

Fairness Vocal Bursts Valence Prediction

Quantifying Infra-Marginality and Its Trade-off with Group Fairness

no code implementations3 Sep 2019 Arpita Biswas, Siddharth Barman, Amit Deshpande, Amit Sharma

To quantify this bias, we propose a general notion of $\eta$-infra-marginality that can be used to evaluate the extent of this bias.

Decision Making Fairness

Fair Division Under Cardinality Constraints

no code implementations25 Apr 2018 Siddharth Barman, Arpita Biswas

In this setting, we are given a partition of the entire set of goods---i. e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each category to any agent.

Fairness

Groupwise Maximin Fair Allocation of Indivisible Goods

no code implementations21 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.

Fairness

Managing Overstaying Electric Vehicles in Park-and-Charge Facilities

no code implementations19 Apr 2016 Arpita Biswas, Ragavendran Gopalakrishnan, Partha Dutta

To analyze this central trade-off, we develop a novel framework that integrates models for realistic user behavior into queueing dynamics to locate the optimal penalty from the points of view of utilization and revenue, for different values of the external charging demand.

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