[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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