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Artificial Intelligence
 
Data Science
 
Algorithms

[Archimedes Talks Series] To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model

Dates
2024-06-13 16:00 - 18:00
Venue
Artemidos 1 - Amphitheater
As part of our regular Prediction Study Group, Archimedes is delighted to host a talk on "To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model" by Artem Tsikiridis(Postdoctoral Researcher at CWI) this Thursday at 4pm

Title: To Trust or Not to Trust: Assignment Mechanisms with Predictions in the Private Graph Model

Presenter: Artem Tsikiridis is a Postdoctoral Researcher in the Networks and Optimization Group at Centrum Wiskunde & Informatica (CWI)

Abstract: The realm of algorithms with predictions has led to the development of several new algorithms that leverage (potentially erroneous) predictions to enhance their performance guarantees. The challenge is to devise algorithms that achieve optimal approximation guarantees as the prediction quality varies from perfect (consistency) to imperfect (robustness). This framework is particularly appealing in mechanism design contexts, where predictions might convey private information about the agents. In this paper, we design strategyproof mechanisms that leverage predictions to achieve improved approximation guarantees for several variants of the Generalized Assignment Problem (GAP) in the private graph model. In this model, first introduced by Dughmi & Ghosh (2010), the set of resources that an agent is compatible with is private information. For the Bipartite Matching Problem (BMP), we give a deterministic group-strategyproof (GSP) mechanism that is (1+1/γ)-consistent and (1+γ)-robust, where γ≥1 is some confidence parameter. We also prove that this is best possible. Remarkably, our mechanism draws inspiration from the renowned Gale-Shapley algorithm, incorporating predictions as a crucial element. Additionally, we give a randomized mechanism that is universally GSP and improves on the guarantees in expectation. The other GAP variants that we consider all make use of a unified greedy mechanism that adds edges to the assignment according to a specific order. Our universally GSP mechanism randomizes over the greedy mechanism, our mechanism for BMP and the predicted assignment, leading to (1+3/γ)-consistency and (3+γ)-robustness in expectation. All our mechanisms also provide more fine-grained approximation guarantees that interpolate between the consistency and the robustness, depending on some natural error measure of the prediction.

Bio: Artem Tsikiridis is a Postdoctoral Researcher in the Networks and Optimization Group at Centrum Wiskunde & Informatica (CWI), where he is hosted by Guido Schäfer. He completed his Ph.D. in 2023 at the Athens University of Economics and Business (AUEB), where he was supervised by Vangelis Markakis. His research interests lie at the intersection of theoretical computer science, microeconomics and operations research, with a particular focus on questions related to auctions, mechanism design and online algorithms.

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Vision

To position Greece as a leading player in AI and Data Science

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Mission

To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world

 

Welcome to ARCHIMEDES, a vibrant research hub connecting the global AI and Data Science research community fostering groundbreaking research in Greece and beyond. Its dedicated core team, comprising lead researchers, affiliated researchers, Post-Docs, PhDs and interns, is committed to advancing basic and applied research in Artificial Intelligence and its supporting disciplines, including Algorithms, Statistics, Learning Theory, and Game Theory organized around 8 core research areas. By collaborating with Greek and Foreign Universities and Research Institutes, ARCHIMEDES disseminates its research findings fostering knowledge exchange and providing enriching opportunities for students. Leveraging AI to address real-world challenges, ARCHIMEDES promotes innovation within the Greek ecosystem and extends its societal impact. Established in January 2022, as a research unit of the Athena Research Center with support from the Committee Greece 2021, ARCHIMEDES is funded for its first four years by the EU Recovery and Resilience Facility (RRF).

 
 

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The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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