2019/01/24 by Tom Zahavy, Shie Mannor, Zahavy, Tom +1 · 3 citations
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1901.08612
openalex publication_date 2019/01/24 · arxiv created 2019/08/11 · arxiv updated 2019/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the neural-linear bandit model for solving sequential decision-making problems with high dimensional side information. Neural-linear bandits leverage the representation power of deep neural networks and combine it with efficient exploration mechanisms, designed for linear contextual bandits, on top of the last hidden layer. Since the representation is being optimized during learning, information regarding exploration with "old" features is lost. Here, we propose the first limited memory neural-linear bandit that is resilient to this phenomenon, which we term catastrophic forgetting. We evaluate our method on a variety of real-world data sets, including regression, classification, and sentiment analysis, and observe that our algorithm is resilient to catastrophic forgetting and achieves superior performance.