206 research outputs found
Decentralized Multi-agent Filtering
This paper addresses the considerations that comes along with adopting
decentralized communication for multi-agent localization applications in
discrete state spaces. In this framework, we extend the original formulation of
the Bayes filter, a foundational probabilistic tool for discrete state
estimation, by appending a step of greedy belief sharing as a method to
propagate information and improve local estimates' posteriors. We apply our
work in a model-based multi-agent grid-world setting, where each agent
maintains a belief distribution for every agents' state. Our results affirm the
utility of our proposed extensions for decentralized collaborative tasks. The
code base for this work is available in the following rep
Fair Algorithms for Hierarchical Agglomerative Clustering
Hierarchical Agglomerative Clustering (HAC) algorithms are extensively
utilized in modern data science, and seek to partition the dataset into
clusters while generating a hierarchical relationship between the data samples.
HAC algorithms are employed in many applications, such as biology, natural
language processing, and recommender systems. Thus, it is imperative to ensure
that these algorithms are fair -- even if the dataset contains biases against
certain protected groups, the cluster outputs generated should not discriminate
against samples from any of these groups. However, recent work in clustering
fairness has mostly focused on center-based clustering algorithms, such as
k-median and k-means clustering. In this paper, we propose fair algorithms for
performing HAC that enforce fairness constraints 1) irrespective of the
distance linkage criteria used, 2) generalize to any natural measures of
clustering fairness for HAC, 3) work for multiple protected groups, and 4) have
competitive running times to vanilla HAC. Through extensive experiments on
multiple real-world UCI datasets, we show that our proposed algorithm finds
fairer clusterings compared to vanilla HAC as well as other state-of-the-art
fair clustering approaches
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