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20232026
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cs.LG2026

Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz +2

Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient on evolving networks where ed…

cs.LG2026

Decision Tree Learning on Product Spaces

Arshia Soltani Moakhar, Faraz Ghahremani, Kiarash Banihashem +1

Decision tree learning has long been a central topic in theoretical computer science, driven by its practical importance. A fundamental and widely used method for decision tree con…

cs.LG2026

Replicable Composition

Kiarash Banihashem, MohammadHossein Bateni, Hossein Esfandiari +2

Replicability requires that algorithmic conclusions remain consistent when rerun on independently drawn data. A central structural question is composition: given problems each…

cs.LG2026

Active Learning for Decision Trees with Provable Guarantees

Arshia Soltani Moakhar, Tanapoom Laoaron, Faraz Ghahremani +2

This paper advances the theoretical understanding of active learning label complexity for decision trees as binary classifiers. We make two main contributions. First, we provide th…

cs.LG2023

An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits

Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin +1

We present an oracle-efficient relaxation for the adversarial contextual bandits problem, where the contexts are sequentially drawn i.i.d from a known distribution and the cost seq…