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[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.
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
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).
Archimedes and the Biomedical Research Foundation Share Latest Findings in AI and Medicine
4 March 2025
Archimedes and the Biomedical Research Foundation of the Academy of Athens successfully hosted a special collaborative session at the Panhellenic Working Group Seminars of the Hellenic Society of Cardiology. This session focused on innovative applications of artificial intelligence in medicine, with a particular emphasis on advancements in cardiology. Held on Friday, February 7, 2025, in Room MC2 of the Megaron Athens International Conference Centre, the event brought together leading experts to explore how artificial intelligence is transforming cardiovascular medicine.
Archimedes Reaches Milestone of 200 Publications
12 February 2025
Archimedes is proud to announce that its researchers have published over 200 scientific publications in top-tier conferences (NeurIPS, ICLR, ICML) and journals.Archimedes maintains a vibrant scientific community of over 130 researchers, including more than 60 senior researchers (faculty members from Greece and abroad), 12 postdoctoral fellows, and 55 PhD students, along with over 20 undergraduate interns from various disciplines.
Happy International Greek Language Day!
9 February 2025
Today, we celebrate the historical, cultural, and linguistic significance of the Greek language. While Standard Modern Greek often takes center stage, we at Archimedes - AI and Data Science Research Hub recognize the impressive diversity and great cultural significance of its numerous dialects. These dialects present both exciting opportunities and complex challenges for AI and Large Language Models (LLMs) because each one of them presents unique linguistic features and all of them are low resourced. That’s why we’re using cutting-edge AI to document, digitize, and analyze these invaluable linguistic treasures, ensuring their preservation and accessibility for generations to come.
Two Research Positions in Data Stream Management Systems & Big Data Management
7 February 2025
We are pleased to announce the availability of two research positions in data stream management systems and big data management, to be co-supervised by Assistant Professor Odysseas Papapetrou from the Eindhoven University of Technology (TU/e) in the Netherlands and Professor Minos Garofalakis from the Technical University of Crete in Greece.
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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