114 citations · 381 across the 40 of their papers we have counts for
45 papers
Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits
Emre Özyıldırım, Barış Yaycı, Umut Eren Akturk +1
We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from finite codebooks, serve multipl…
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar
Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeli…
Robust Bayesian Satisficing
Artun Saday, Yaşar Cahit Yıldırım, Cem Tekin
Distributional shifts pose a significant challenge to achieving robustness in contemporary machine learning. To overcome this challenge, robust satisficing (RS) seeks a robust solu…
Robust Pareto Set Identification with Contaminated Bandit Feedback
İlter Onat Korkmaz, Efe Eren Ceyani, Kerem Bozgan +1
We consider the Pareto set identification (PSI) problem in multi-objective multi-armed bandits (MO-MAB) with contaminated reward observations. At each arm pull, with some fixed pro…
Federated Multi-Armed Bandits Under Byzantine Attacks
Artun Saday, İlker Demirel, Yiğit Yıldırım +1
Multi-armed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward.…
Safe Linear Leveling Bandits
Ilker Demirel, Mehmet Ufuk Ozdemir, Cem Tekin
Multi-armed bandits (MAB) are extensively studied in various settings where the objective is to \textit{maximize} the actions' outcomes (i.e., rewards) over time. Since safety is c…