5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Offline Local Search for Online Stochastic Bandits
Gerdus Benadè, Rathish Das, Thomas Lavastida
Combinatorial multi-armed bandits provide a fundamental online decision-making environment where a decision-maker interacts with an environment across time steps, each time sel…
cs.LG2024
Binary Search with Distributional Predictions
Michael Dinitz, Sungjin Im, Thomas Lavastida +3
Algorithms with (machine-learned) predictions is a powerful framework for combining traditional worst-case algorithms with modern machine learning. However, the vast majority of wo…
cs.LG2021★ 5 cited
Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida +2
A recent line of research investigates how algorithms can be augmented with machine-learned predictions to overcome worst case lower bounds. This area has revealed interesting algo…