47 citations · 102 across the 27 of their papers we have counts for
31 papers
Meritocratic Fairness via -Shapley Values in Budgeted Combinatorial Bandits with Full-Bandit Feedback
Shradha Sharma, Shweta Jain, Swapnil Dhamal
We study meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback, where a learner selects at most arms per time step and observes only the…
Lipschitz Dueling Bandits over Continuous Action Spaces
Mudit Sharma, Shweta Jain, Vaneet Aggarwal +1
We study for the first time, stochastic dueling bandits over continuous action spaces with Lipschitz structure, where feedback is purely comparative. While dueling bandits and Lips…
Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback
Subham Pokhriyal, Shweta Jain, Vaneet Aggarwal
We study the \emph{Submodular Welfare Problem} (SWP), where items are partitioned among agents with monotone submodular utilities to maximize the total welfare under \emph{bandit f…
The Multi-Stage Assignment Problem: A Fairness Perspective
Vibulan J, Swapnil Dhamal, Shweta Jain
This paper explores the problem of fair assignment on Multi-Stage graphs. A multi-stage graph consists of nodes partitioned into disjoint sets (stages) structured as a sequence…
A Resilience Framework for Bi-Criteria Combinatorial Optimization with Bandit Feedback
Vaneet Aggarwal, Shweta Jain, Subham Pokhriyal +1
We study bi-criteria combinatorial optimization under noisy function evaluations. While resilience and black-box offline-to-online reductions have been studied in single-objective…
Towards Fairness in Provably Communication-Efficient Federated Recommender Systems
Kirandeep Kaur, Sujit Gujar, Shweta Jain
To reduce the communication overhead caused by parallel training of multiple clients, various federated learning (FL) techniques use random client sampling. Nonetheless, ensuring t…
Fairness of Exposure in Online Restless Multi-armed Bandits
Archit Sood, Shweta Jain, Sujit Gujar
Restless multi-armed bandits (RMABs) generalize the multi-armed bandits where each arm exhibits Markovian behavior and transitions according to their transition dynamics. Solutions…
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits
Subham Pokhriyal, Shweta Jain, Ganesh Ghalme +2
Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribu…
Fairness and Privacy Guarantees in Federated Contextual Bandits
Sambhav Solanki, Shweta Jain, Sujit Gujar
This paper considers the contextual multi-armed bandit (CMAB) problem with fairness and privacy guarantees in a federated environment. We consider merit-based exposure as the desir…
A Novel Demand Response Model and Method for Peak Reduction in Smart Grids -- PowerTAC
Sanjay Chandlekar, Arthik Boroju, Shweta Jain +1
One of the widely used peak reduction methods in smart grids is demand response, where one analyzes the shift in customers' (agents') usage patterns in response to the signal from…
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…
Budgeted Combinatorial Multi-Armed Bandits
Debojit Das, Shweta Jain, Sujit Gujar
We consider a budgeted combinatorial multi-armed bandit setting where, in every round, the algorithm selects a super-arm consisting of one or more arms. The goal is to minimize the…
Exploring and Mitigating Gender Bias in Recommender Systems with Explicit Feedback
Shrikant Saxena, Shweta Jain
Recommender systems are indispensable because they influence our day-to-day behavior and decisions by giving us personalized suggestions. Services like Kindle, Youtube, and Netflix…
Effect of damped oscillations in the inflationary potential
Akhil Antony, Shweta Jain
We investigate the effect of damped oscillations on a nearly flat inflationary potential and the features they produce in the power-spectrum and bi-spectrum. We compare the model w…
An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System
Ajay Gangwar, Shweta Jain
The observed ratings in most recommender systems are subjected to popularity bias and are thus not randomly missing. Due to this, only a few popular items are recommended, and a va…
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…
Storage Aware Routing for Generalized Delay Tolerant Networks
Shweta Jain, Snehapreethi Gopinath, Dipankar Raychaudhuri
This paper presents a novel storage aware routing (STAR) protocol designed to provide a general networking solution over a broad range of wired and wireless usage scenarios. STAR e…
MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers
Rajat Sharma, Nikhil Reddy, Vidhya Kamakshi +2
The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-…
FPT Algorithms for Finding Near-Cliques in -Closed Graphs
Balaram Behera, Edin Husić, Shweta Jain +2
Finding large cliques or cliques missing a few edges is a fundamental algorithmic task in the study of real-world graphs, with applications in community detection, pattern recognit…
Provably and Efficiently Approximating Near-cliques using the Turán Shadow: PEANUTS
Shweta Jain, C. Seshadhri
Clique and near-clique counts are important graph properties with applications in graph generation, graph modeling, graph analytics, community detection among others. They are the…
A Pitfall of Learning from User-generated Data: In-depth Analysis of Subjective Class Problem
Kei Nemoto, Shweta Jain
Research in the supervised learning algorithms field implicitly assumes that training data is labeled by domain experts or at least semi-professional labelers accessible through cr…
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions
Kumar Abhishek, Shweta Jain, Sujit Gujar
For sponsored search auctions, we consider contextual multi-armed bandit problem in the presence of strategic agents. In this setting, at each round, an advertising platform (cente…
The Power of Pivoting for Exact Clique Counting
Shweta Jain, C. Seshadhri
Clique counting is a fundamental task in network analysis, and even the simplest setting of -cliques (triangles) has been the center of much recent research. Getting the count o…
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 (…