Session 1:
Invited Talk
Debmalya Panigrahi: Algorithms with Predictions: What Comes Next?
Abstract: Algorithms with predictions have emerged as one of the most influential developments in algorithms research over the past decade. By augmenting classical algorithmic decision-making with machine-learned forecasts, this paradigm has led to significant advances across a wide range of optimization and online decision problems. As the field matures, however, it is natural to ask: what are the next fundamental questions and challenges that will shape its future?
In this talk, I will discuss two research directions that I find particularly exciting. The first part will explore the search for broad algorithmic principles and design frameworks that transcend individual problems, moving beyond techniques tailored to specific applications toward a more unified theory of algorithms with predictions. The second part will focus on robustness: how predictions fail, the different ways in which they can be unreliable, and how algorithms can be designed to recover gracefully from such errors while retaining strong performance guarantees.
The talk will provide a self-contained overview of these directions and highlight opportunities for future research in these areas.
Contributed Talk
Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis, Giorgos Mitropoulos, Panagiotis Patsilinakos, Michalis Xefteris: Solving NP-Hard Problems with Noisy Predictions
Session 2:
Contributed Talks
Hao-Yuan He, Jiang-Tian Xue and Ming Li: Learning-Augmented Smooth Integer Programs with PAC-Learnable Oracles
Antonios Antoniadis, Ali Shahheidar, Golnoosh Shahkarami and Abolfazl Soltani: A Switching Framework for Online Interval Scheduling with Predictions
Magnus Berg, Joan Boyar, Lene M. Favrholdt and Kim S. Larsen: On the Complexity of Online Problems with Predictions
Session 3:
Invited Talk
Vianney Perchet: "The Price of Smoothness", in learning-augmented first-price auctions
Abstract: In first-price auctions, bidders must decide how much to shade their bid below their value — bid too low and you lose, bid too high and you leave money on the table. A natural idea is to use predictions of the competition, but first-price auctions have an awkward feature: get the prediction wrong by even a little and you can lose everything, making it hard for prediction-based strategies to fail gracefully. In this talk, I'll discuss what performance guarantees are actually achievable once we insist on this kind of graceful degradation. I'll show that in a single auction, smoothness comes at a real cost to performance under good predictions — but that repeated play offers a way out, via a simple algorithm that adaptively learns how much to trust its predictions.
Contributed Talk
Romain Cosson, Jingwei Li, Alexander Lindermayr and Jens Schlöter: Online Scheduling with Stochastic Clairvoyance
Session 4:
Contributed Talks
Toby Walsh: Mechanism Design for Facility Location using Predictions
Changyeol Lee, Dahoon Lee, Jongseo Lee, Yongho Shin and Changki Yun: Optimal Learning-Augmented Algorithm for Online Bidding
Yishu Wang, Yuxuan Wang and Hanyang Tang: Beyond Forecasting: The Belief-to-Trade Layer in Prediction-Market Agents