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Artificial Intelligence
 
Data Science
 
Algorithms

[Archimedes Mechanism Design Theme invited talk]Learning in Budgeted Auctions with Spacing Objectives

Dates
2025-01-22 14:30 - 16:00
Venue
Artemidos 1 - Amphitheater
Title: Learning in Budgeted Auctions with Spacing Objectives

Speaker: Giannis Fikioris (PhD Student at Cornell University, USA)

Abstract: In many repeated auction settings, participants care not only about how frequently they win but also how their winnings are distributed over time. This problem arises in various practical domains where avoiding congested demand is crucial, such as online retail sales and compute services, as well as in advertising campaigns that require sustained visibility over time. We introduce a simple model of this phenomenon, modeling it as a budgeted auction where the value of a win is a concave function of the time since the last win. This implies that for a given number of wins, even spacing over time is optimal. The goal is to maximize and evenly space conversions rather than just wins.

We study the optimal policies for this setting in second-price auctions and offer learning algorithms for the bidders that achieve low regret against the optimal bidding policy in a Bayesian online setting. Our main result is a computationally efficient online learning algorithm that achieves O(sqrt(T)) regret. We achieve this by showing that an infinite-horizon Markov decision process (MDP) with the budget constraint in expectation is essentially equivalent to our problem, even when limiting that MDP to a very small number of states. The algorithm achieves low regret by learning a bidding policy that chooses bids as a function of the context and the system's state, which will be the time elapsed since the last win (or conversion).

Short Bio: Giannis Fikioris  is a 5th-year PhD student in the department of Computer Science at Cornell University, advised by Eva Tardos. Before that, he got his diploma from NTUA, advised by Dimitris Fotakis. His interests include mainly Algorithmic Game Theory and Online Learning. Specifically, he has worked on Online Learning with Constraints, Online Resource Allocation, and Repeated Budgeted Auctions. His PhD has been supported by the NDSEG fellowship, the Onassis Scholarship, and the Google PhD fellowship.


You can attend remotely via MS-Teams through:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_NzE4MjZkZGItZWE3NS00NzQ5LTk0OTItNWMzMzE3NjdkYjU4%40thread.v2/0?context=%7b%22Tid%22%3a%226ae07702-c5f7-4f38-9b87-acad62a75d93%22%2c%22Oid%22%3a%22700280a8-c91f-41bd-9bf8-38b7959b4a6f%22%7d   







 
 

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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More news items here
 
 

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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