192 citations · 336 across the 18 of their papers we have counts for
20 papers
Maximin Share Guarantees via Limited Cost-Sensitive Sharing
Hana Salavcova, Martin Černý, Arpita Biswas
We study the problem of fairly allocating indivisible goods when limited sharing is allowed, that is, each good may be allocated to up to agents, while incurring a cost for sha…
AgroAskAI: A Multi-Agentic AI Framework for Supporting Smallholder Farmers' Enquiries Globally
Nadine Angela Cantonjos, Arpita Biswas
Agricultural regions in rural areas face damage from climate-related risks, including droughts, heavy rainfall, and shifting weather patterns. Prior research calls for adaptive ris…
An Algorithmic Approach to Address Course Enrollment Challenges
Arpita Biswas, Yiduo Ke, Samir Khuller +1
Massive surges of enrollments in courses have led to a crisis in several computer science departments - not only is the demand for certain courses extremely high from majors, but t…
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks
Arpita Biswas, Jackson A. Killian, Paula Rodriguez Diaz +2
Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource con…
Ranked Prioritization of Groups in Combinatorial Bandit Allocation
Lily Xu, Arpita Biswas, Fei Fang +1
Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have…
Towards Fair Recommendation in Two-Sided Platforms
Arpita Biswas, Gourab K Patro, Niloy Ganguly +2
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. Trad…
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning
Jackson A. Killian, Lily Xu, Arpita Biswas +1
We introduce robustness in \textit{restless multi-armed bandits} (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly…
Q-Learning Lagrange Policies for Multi-Action Restless Bandits
Jackson A. Killian, Arpita Biswas, Sanket Shah +1
Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which independent processes are managed. However, previous wor…
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare
Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham +1
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may g…
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems
Aditya Mate, Arpita Biswas, Christoph Siebenbrunner +2
We propose Streaming Bandits, a Restless Multi Armed Bandit (RMAB) framework in which heterogeneous arms may arrive and leave the system after staying on for a finite lifetime. Str…
On Achieving Leximin Fairness and Stability in Many-to-One Matchings
Shivika Narang, Arpita Biswas, Y Narahari
The past few years have seen a surge of work on fairness in allocation problems where items must be fairly divided among agents having individual preferences. In comparison, fairne…
Ensuring Fairness under Prior Probability Shifts
Arpita Biswas, Suvam Mukherjee
In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set. This phenomen…
COVID-19: Strategies for Allocation of Test Kits
Arpita Biswas, Shruthi Bannur, Prateek Jain +1
With the increasing spread of COVID-19, it is important to systematically test more and more people. The current strategy for test-kit allocation is mostly rule-based, focusing on…
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
Gourab K Patro, Arpita Biswas, Niloy Ganguly +2
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. Traditionally, recommen…
Quantifying Infra-Marginality and Its Trade-off with Group Fairness
Arpita Biswas, Siddharth Barman, Amit Deshpande +1
In critical decision-making scenarios, optimizing accuracy can lead to a biased classifier, hence past work recommends enforcing group-based fairness metrics in addition to maximiz…
Fair Division Under Cardinality Constraints
Siddharth Barman, Arpita Biswas
We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations. In this setting, we are given a partition of th…
Groupwise Maximin Fair Allocation of Indivisible Goods
Siddharth Barman, Arpita Biswas, Sanath Kumar Krishnamurthy +1
We study the problem of allocating indivisible goods among n agents in a fair manner. For this problem, maximin share (MMS) is a well-studied solution concept which provides a fair…
Impact of Detour-Aware Policies on Maximizing Profit in Ridesharing
Arpita Biswas, Ragavendran Gopalakrishnan, Theja Tulabandhula +3
This paper provides efficient solutions to maximize profit for commercial ridesharing services, under a pricing model with detour-based discounts for passengers. We propose greedy…
Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations
Ragavendran Gopalakrishnan, Arpita Biswas, Alefiya Lightwala +3
Effective placement of charging stations plays a key role in Electric Vehicle (EV) adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be…
Managing Overstaying Electric Vehicles in Park-and-Charge Facilities
Arpita Biswas, Ragavendran Gopalakrishnan, Partha Dutta
With the increase in adoption of Electric Vehicles (EVs), proper utilization of the charging infrastructure is an emerging challenge for service providers. Overstaying of an EV aft…