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Found 17 papers, 5 papers with code
Fairness of Exposure in Online Restless Multi-armed Bandits
However, they do not consider the distribution of pulls among the arms.
Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits
At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group.
Fairness and Privacy Guarantees in Federated Contextual Bandits
We design a novel communication protocol that allows for (i) Sub-linear theoretical bounds on fairness regret for Fed-FairX-LinUCB and comparable bounds for the private counterpart, Priv-FairX-LinUCB (relative to single-agent learning), (ii) Effective use of privacy budget in Priv-FairX-LinUCB.
A Novel Demand Response Model and Method for Peak Reduction in Smart Grids -- PowerTAC
Finally, we showcase the efficacy of the proposed algorithm in mitigating demand peaks in a real-world smart grid system using the PowerTAC simulator as a test bed.
Budgeted Combinatorial Multi-Armed Bandits
With this reduction, we propose CBwK-LPUCB that uses PrimalDualBwK ingeniously.
Exploring and Mitigating Gender Bias in Recommender Systems with Explicit Feedback
We propose a model to quantify the gender bias present in book rating datasets and in the recommendations produced by the recommender systems.
A Highly Configurable Hardware/Software Stack for DNN Inference Acceleration
We have enhanced the VTA design space and enabled end-to-end support for additional workloads.
An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System
With the help of comprehensive experiments on real-world datasets, we show that our proposed algorithm outperforms the existing algorithms on the proposed popularity bias metric.
Efficient Algorithms For Fair Clustering with a New Fairness Notion
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 cluster; i. e., a balance property.
MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers
Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined.
A Pitfall of Learning from User-generated Data: In-depth Analysis of Subjective Class Problem
Using this framework, data mining practitioners can detect a subjective class at an early stage of their projects, and avoid wasting their precious time and resources by dealing with subjective class problem with traditional machine learning techniques.
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions
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).
Ballooning Multi-Armed Bandits
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model.
A Fast and Provable Method for Estimating Clique Counts Using Turán's Theorem
Most methods used for triangle counting do not scale for large cliques, and existing algorithms require massive parallelism to be feasible.
Social and Information Networks Data Structures and Algorithms
A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing
The time to failure of a worker depends on the duration of the task handled by the worker.
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
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).
Image Optimization and Prediction
Image Processing, Optimization and Prediction of an Image play a key role in Computer Science.
