Professor of Operations Research, ESSEC Business School of Paris

Talk: Bilevel Optimization Under Uncertainty: Challenges and Opportunities

Thanks to significant algorithmic advances in the field of computational bilevel optimization, today we can solve much larger and more complicated bilevel problems compared to what was possible two decades ago. In this talk, we will focus on one of the emerging and challenging classes of bilevel problems: bilevel optimization under uncertainty. We will discuss classical ways of addressing uncertainties in bilevel optimization using stochastic or robust optimization techniques. However, the sources of uncertainty in bilevel optimization can be much richer than for usual, single-level problems, since not only the problem’s data can be uncertain but also the (observation of the) decisions of the two players can be subject to uncertainty. Thus, we will also discuss bilevel optimization under limited observability, the area of problems considering only near-optimal decisions, and intermediate solution concepts between the optimistic and pessimistic cases.

The talk is based on the two articles by [1, 2].

References

[1] Yasmine Beck, Ivana Ljubic, Martin Schmidt, A Survey on Bilevel Optimization Under Uncertainty, European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2023.01.008 (2023)

 [2] Yasmine Beck, Ivana Ljubic, Martin Schmidt, A Brief Introduction to Robust Bilevel Optimization, SIAG/OPT Views and News, 30(2), (2023) https://siagoptimization.github.io/assets/views/ViewsAndNews-30-2.pdf

Bio: Ivana Ljubić is Professor of Operations Research at the ESSEC Business School of Paris. She holds a PhD degree in computer science from the Vienna University of Technology (2004). Prior to joining ESSEC in 2015, she was appointed at the University of Vienna, where she also received her habilitation in Operations Research in 2013. Research interests of Ivana Ljubic include combinatorial optimization, optimization under uncertainty and bilevel optimization, with applications in network design, telecommunications, and logistics.  She is member of Editorial Board of European Journal of Operational Research, Computers & Operations Research and she is Associate Editor for Transportation Science and Networks. 

Associate Professor in Computer Science, University of Maryland

Chief Scientist, Arthur

Talk: Robustness, Privacy, Fairness, and Credibility? Pushing the Boundaries of Economic Design with Deep Learning

The design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectrum allocation, and myriad financial markets. Analytic progress in this space is notoriously difficult; since Myerson's 1981 work characterizing single-item optimal auctions, there has been limited progress outside of restricted settings. Motivated by this analytic difficulty, we will instead discuss differentiable economic design: using deep learning techniques to approximate optimal auctions. Differentiable economic design introduces a fresh host of opportunities for research at the intersection of operations research, AI/ML, and microeconomics. We discuss recent progress in some of these open areas, including:

* fairness while maintaining high revenue and strong incentive guarantees, including learning fairness from human preferences;

* certified robustness, that is, verification of claimed strategyproofness of deep learned auctions; and

* expressiveness via different demand functions and other constraints.

Throughout, we highlight areas where the CPAIOR community's expertise could accelerate progress, or open up entirely new research directions.

Bio: John P Dickerson is co-founder and Chief Scientist of Arthur, the AI performance monitoring company, as well as Associate Professor of Computer Science at the University of Maryland.  He is a recipient of awards such as the NSF CAREER Award, IEEE Intelligent Systems AI's 10 to Watch, Google Faculty Research Award, Google AI for Social Good Award, and paper awards and nominations at venues such as AAAI.  His research centers on solving practical economic problems using techniques from computer science, stochastic optimization, and machine learning. He has worked extensively on theoretical and empirical approaches to organ exchange where his work has set policy at the UNOS nationwide kidney exchange; worldwide blood donation markets with Facebook; game-theoretic approaches to counter-terrorism and negotiation, where his models have been deployed; and market design problems in industry (e.g., online advertising) through various startups.  He received his PhD in computer science from Carnegie Mellon University.

Head of Research, Amadeus

Talk:  OR and AI Applications in the Travel Industry

This invited talk aims to provide a comprehensive overview of the latest applications and ongoing research directions of Operations Research (OR) and Artificial Intelligence (AI) in the travel industry. We will discuss the main challenges faced by the travel industry actors (such as airlines, hotels, travel agencies among others) and how OR and AI is being used during the whole customer journey. This includes applications from the inspiration phase (where and when to travel?), search and booking (which flights to propose? which hotels? how to price tickets? Which alternatives to recommend?), the experience at the airports and during the trip (check-in, dealing with cancellations and delays), and finally the post travel activities (travel expenses for business trips, or post trip feed.

In terms of techniques, we will discuss discrete optimization problems, unsupervised and supervised machine learning, generative models, reinforcement learning, touching generic application areas such as NLP (natural language processing), Computer Vision, Recommender Systems among others.

Bio:

Rodrigo ACUNA AGOST has over 15 years of expertise in Artificial Intelligence and Operations Research. He currently leads the Amadeus  Research Department based in Nice area (France). He is responsible for exploratory and applied research activities for the travel industry, which includes for example IT solutions for airlines, airports, and travel agencies.

Rodrigo and his team have successfully initiated several research projects that are now implemented and have a significant impact on the travel industry. He is highly committed to the research community and Academia, he has presented his works at several international conferences, published multiple research papers and patents in these fields and also teaching AI Management at SKEMA Business School.