19 citations · 40 across the 13 of their papers we have counts for
14 papers
Designing Fair, Cost-optimal Auctions based on Deep Learning for Procuring Agricultural Inputs through Farmer Collectives
Mayank Ratan Bhardwaj, Bazil Ahmed, Prathik Diwakar +2
Procuring agricultural inputs (agri-inputs for short) such as seeds, fertilizers, and pesticides, at desired quality levels and at affordable cost, forms a critical component of ag…
A Discrete and Bounded Locally Envy-Free Cake Cutting Protocol on Trees
Ganesh Ghalme, Xin Huang, Yuka Machino +1
We study the classic problem of \emph{fairly} dividing a heterogeneous and divisible resource -- modeled as a line segment and typically called as a \emph{cake} -- among $n…
Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee for Improving Bandits
Vishakha Patil, Vineet Nair, Ganesh Ghalme +1
We study the Improving Multi-Armed Bandit (IMAB) problem, where the reward obtained from an arm increases with the number of pulls it receives. This model provides an elegant abstr…
Strategic Representation
Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen +1
Humans have come to rely on machines for reducing excessive information to manageable representations. But this reliance can be abused -- strategic machines might craft representat…
Envy-Free Cake Cutting with Graph Constraints
Ganesh Ghalme, Xin Huang, Nidhi Rathi
We study the classic problem of fairly dividing a heterogeneous and divisible resource -- represented by a cake, -- among agents. This work considers an interesting var…
Individual Fairness in Feature-Based Pricing for Monopoly Markets
Shantanu Das, Swapnil Dhamal, Ganesh Ghalme +2
We study fairness in the context of feature-based price discrimination in monopoly markets. We propose a new notion of individual fairness, namely, α-fairness, which guarantees tha…
Efficient Algorithms For Fair Clustering with a New Fairness Notion
Shivam Gupta, Ganesh Ghalme, Narayanan C. Krishnan +1
We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every clust…
Sleeping Combinatorial Bandits
Kumar Abhishek, Ganesh Ghalme, Sujit Gujar +1
In this paper, we study an interesting combination of sleeping and combinatorial stochastic bandits. In the mixed model studied here, at each discrete time instant, an arbitrary \e…
Strategic Classification in the Dark
Ganesh Ghalme, Vineet Nair, Itay Eilat +2
Strategic classification studies the interaction between a classification rule and the strategic agents it governs. Under the assumption that the classifier is known, rational agen…
Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
Ganesh Ghalme, Vineet Nair, Vishakha Patil +1
Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare. In this work, we study fairness in…
Ballooning Multi-Armed Bandits
Ganesh Ghalme, Swapnil Dhamal, Shweta Jain +2
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (…
Achieving Fairness in the Stochastic Multi-armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair +1
We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of…
Achieving Fairness in Stochastic Multi-armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair +1
We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of…
Fair Division of Indivisible Goods Among Strategic Agents
Siddharth Barman, Ganesh Ghalme, Shweta Jain +2
We study fair division of indivisible goods in a single-parameter environment. In particular, we develop truthful social welfare maximizing mechanisms for fairly allocating indivis…