Search Results for author: Sujit Gujar

Found 15 papers, 0 papers with code

Differentially Private Federated Combinatorial Bandits with Constraints

no code implementations27 Jun 2022 Sambhav Solanki, Samhita Kanaparthy, Sankarshan Damle, Sujit Gujar

There is a rapid increase in the cooperative learning paradigm in online learning settings, i. e., federated learning (FL).

Federated Learning online learning +1

Budgeted Combinatorial Multi-Armed Bandits

no code implementations8 Feb 2022 Debojit Das, Shweta Jain, Sujit Gujar

With this reduction, we propose CBwK-LPUCB that uses PrimalDualBwK ingeniously.

Multi-Armed Bandits

F3: Fair and Federated Face Attribute Classification with Heterogeneous Data

no code implementations6 Sep 2021 Samhita Kanaparthy, Manisha Padala, Sankarshan Damle, Ravi Kiran Sarvadevabhatla, Sujit Gujar

F3 adopts multiple heuristics to improve fairness across different demographic groups without requiring data homogeneity assumption.

Classification Fairness +1

Federated Learning Meets Fairness and Differential Privacy

no code implementations23 Aug 2021 Manisha Padala, Sankarshan Damle, Sujit Gujar

Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy.

Fairness Federated Learning

Sleeping Combinatorial Bandits

no code implementations3 Jun 2021 Kumar Abhishek, Ganesh Ghalme, Sujit Gujar, Yadati Narahari

An algorithm can select a subset of arms from the \emph{availability set} (sleeping bandits) and receive the corresponding reward along with semi-bandit feedback (combinatorial bandits).

A Multi-Arm Bandit Approach To Subset Selection Under Constraints

no code implementations9 Feb 2021 Ayush Deva, Kumar Abhishek, Sujit Gujar

We show that after a certain number of rounds, $\tau$, \newalgo\ outputs a subset of agents that satisfy the average quality constraint with a high probability.

We might walk together, but I run faster: Network Fairness and Scalability in Blockchains

no code implementations8 Feb 2021 Anurag Jain, Shoeb Siddiqui, Sujit Gujar

Due to varying network capacities, the slower nodes would be at a relative disadvantage compared to the faster ones, which could negatively impact their revenue.

Fairness Distributed, Parallel, and Cluster Computing Networking and Internet Architecture

Effect of Input Noise Dimension in GANs

no code implementations15 Apr 2020 Manisha Padala, Debojit Das, Sujit Gujar

We aim to quantitatively and qualitatively study the effect of the dimension of the input noise on the performance of GANs.

Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions

no code implementations26 Feb 2020 Kumar Abhishek, Shweta Jain, Sujit Gujar

It is in the best interest of the center to select an ad that has a high expected value (i. e., probability of getting a click $\times$ value it derives from a click of the ad).

Multi-Armed Bandits

Ballooning Multi-Armed Bandits

no code implementations24 Jan 2020 Ganesh Ghalme, Swapnil Dhamal, Shweta Jain, Sujit Gujar, Y. Narahari

In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model.

Multi-Armed Bandits

Introduction to Concentration Inequalities

no code implementations4 Oct 2019 Kumar Abhishek, Sneha Maheshwari, Sujit Gujar

In this report, we aim to exemplify concentration inequalities and provide easy to understand proofs for it.

FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings (Extended Version)

no code implementations10 Jun 2019 Moin Hussain Moti, Dimitris Chatzopoulos, Pan Hui, Sujit Gujar

FaRM uses \textit{(i)} a \emph{report strength score} to remove the risk of random pairing with dishonest reporters, \textit{(ii)} a \emph{consistency score} to measure an agent's history of accurate reports and distinguish valuable reports, \textit{(iii)} a \emph{reliability score} to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and \textit{(iv)} a \emph{location robustness score} to filter agents who try to participate without being present in the considered setting.


FNNC: Achieving Fairness through Neural Networks

no code implementations1 Nov 2018 Padala Manisha, Sujit Gujar

Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging.

Classification Decision Making +2

Generative Adversarial Networks (GANs): What it can generate and What it cannot?

no code implementations31 Mar 2018 P Manisha, Sujit Gujar

We compare and contrast different results and put forth a summary of theoretical contributions about GANs with focus on image/visual applications.

An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance

no code implementations27 Jun 2014 Shweta Jain, Sujit Gujar, Satyanath Bhat, Onno Zoeter, Y. Narahari

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).

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