11 papers
The Pareto Frontier of Randomized Learning-Augmented Online Bidding
Mathis Degryse, Imrane Saakour, Christoph Dürr +1
Online bidding is a classical problem in online decision-making, with applications in resource allocation, hierarchical clustering, and the analysis of approximation algorithms. We…
Learning-Augmented Online Bidding in Stochastic Settings
Spyros Angelopoulos, Bertrand Simon
Online bidding is a classic optimization problem, with several applications in online decision-making, the design of interruptible systems, and the analysis of approximation algori…
Decision-Theoretic Approaches for Improved Learning-Augmented Algorithms
Spyros Angelopoulos, Christoph Dürr, Georgii Melidi
We initiate the systematic study of decision-theoretic metrics in the design and analysis of algorithms with machine-learned predictions. We introduce approaches based on both dete…
Cache Management for Mixture-of-Experts LLMs -- extended version
Spyros Angelopoulos, Loris Marchal, Adrien Obrecht +1
Large language models (LLMs) have demonstrated remarkable capabilities across a variety of tasks. One of the main challenges towards the successful deployment of LLMs is memory man…
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
Ziyad Benomar, Lorenzo Croissant, Vianney Perchet +1
One-max search is a classic problem in online decision-making, in which a trader acts on a sequence of revealed prices and accepts one of them irrevocably to maximise its profit. T…
Search Games with Predictions
Spyros Angelopoulos, Thomas Lidbetter, Konstantinos Panagiotou
We introduce the study of search games between a mobile Searcher and an immobile Hider in a new setting in which the Searcher has some potentially erroneous information, i.e., a pr…