From 9e0bea2c8a4715c2cedff106a1758c5387d6ced6 Mon Sep 17 00:00:00 2001
From: Neil Lawrence <lawrennd@gmail.com>
Date: Sun, 30 Jun 2024 09:05:13 +0100
Subject: [PATCH] Fix Hanna paper

---
 _posts/2024-04-18-a-hanna24a.md               |  57 ------------------
 .../a-hanna24a.pdf => hanna24a/hanna24a.pdf   | Bin
 2 files changed, 57 deletions(-)
 delete mode 100644 _posts/2024-04-18-a-hanna24a.md
 rename a-hanna24a/a-hanna24a.pdf => hanna24a/hanna24a.pdf (100%)

diff --git a/_posts/2024-04-18-a-hanna24a.md b/_posts/2024-04-18-a-hanna24a.md
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----
-title: " Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels "
-software: " https://github.com/mervekarakas/mamab_erasures "
-abstract: " Multi-Armed Bandit (MAB) systems are witnessing an upswing in applications
-  within multi-agent distributed environments, leading to the advancement of collaborative
-  MAB algorithms. In such settings, communication between agents executing actions
-  and the primary learner making decisions can hinder the learning process. A prevalent
-  challenge in distributed learning is action erasure, often induced by communication
-  delays and/or channel noise. This results in agents possibly not receiving the intended
-  action from the learner, subsequently leading to misguided feedback. In this paper,
-  we introduce novel algorithms that enable learners to interact concurrently with
-  distributed agents across heterogeneous action erasure channels with different action
-  erasure probabilities. We illustrate that, in contrast to existing bandit algorithms,
-  which experience linear regret, our algorithms assure sub-linear regret guarantees.
-  Our proposed solutions are founded on a meticulously crafted repetition protocol
-  and scheduling of learning across heterogeneous channels. To our knowledge, these
-  are the first algorithms capable of effectively learning through heterogeneous action
-  erasure channels. We substantiate the superior performance of our algorithm through
-  numerical experiments, emphasizing their practical significance in addressing issues
-  related to communication constraints and delays in multi-agent environments. "
-layout: inproceedings
-series: Proceedings of Machine Learning Research
-publisher: PMLR
-issn: 2640-3498
-id: a-hanna24a
-month: 0
-tex_title: " Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels "
-firstpage: 3898
-lastpage: 3906
-page: 3898-3906
-order: 3898
-cycles: false
-bibtex_author: A Hanna, Osama and Karakas, Merve and Yang, Lin and Fragouli, Christina
-author:
-- given: Osama
-  family: A Hanna
-- given: Merve
-  family: Karakas
-- given: Lin
-  family: Yang
-- given: Christina
-  family: Fragouli
-date: 2024-04-18
-address:
-container-title: Proceedings of The 27th International Conference on Artificial Intelligence
-  and Statistics
-volume: '238'
-genre: inproceedings
-issued:
-  date-parts:
-  - 2024
-  - 4
-  - 18
-pdf: https://proceedings.mlr.press/v238/a-hanna24a/a-hanna24a.pdf
-extras: []
-# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
----
diff --git a/a-hanna24a/a-hanna24a.pdf b/hanna24a/hanna24a.pdf
similarity index 100%
rename from a-hanna24a/a-hanna24a.pdf
rename to hanna24a/hanna24a.pdf