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Found 21 papers, 3 papers with code
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).
Generative Adversarial Networks (GANs): What it can generate and What it cannot?
We compare and contrast different results and put forth a summary of theoretical contributions about GANs with focus on image/visual applications.
FNNC: Achieving Fairness through Neural Networks
Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging.
FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings (Extended Version)
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.
Introduction to Concentration Inequalities
In this report, we aim to exemplify concentration inequalities and provide easy to understand proofs for it.
Ballooning Multi-Armed Bandits
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model.
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).
Effect of Input Noise Dimension in GANs
We aim to quantitatively and qualitatively study the effect of the dimension of the input noise on the performance of GANs.
We might walk together, but I run faster: Network Fairness and Scalability in Blockchains
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
A Multi-Arm Bandit Approach To Subset Selection Under Constraints
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.
Sleeping Combinatorial Bandits
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).
Federated Learning Meets Fairness and Differential Privacy
Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy.
F3: Fair and Federated Face Attribute Classification with Heterogeneous Data
F3 adopts multiple heuristics to improve fairness across different demographic groups without requiring data homogeneity assumption.
Budgeted Combinatorial Multi-Armed Bandits
With this reduction, we propose CBwK-LPUCB that uses PrimalDualBwK ingeniously.
Differentially Private Federated Combinatorial Bandits with Constraints
There is a rapid increase in the cooperative learning paradigm in online learning settings, i. e., federated learning (FL).
Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation
Further, funding the optimal social welfare subset of projects is desirable when every available project cannot be funded due to budget restrictions.
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.
Designing Redistribution Mechanisms for Reducing Transaction Fees in Blockchains
As these blockchains are a public resource, it may be preferable to reduce these transaction fees.
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.
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 of Exposure in Online Restless Multi-armed Bandits
However, they do not consider the distribution of pulls among the arms.
