6 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
Dynamic Non-monotone Submodular Maximization
Kiarash Banihashem, Leyla Biabani, Samira Goudarzi +3
Maximizing submodular functions has been increasingly used in many applications of machine learning, such as data summarization, recommendation systems, and feature selection. More…
Dynamic Constrained Submodular Optimization with Polylogarithmic Update Time
Kiarash Banihashem, Leyla Biabani, Samira Goudarzi +3
Maximizing a monotone submodular function under cardinality constraint is a core problem in machine learning and database with many basic applications, including video and data…
Optimal Sparse Recovery with Decision Stumps
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Max Springer
Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for fe…
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks
Keivan Rezaei, Kiarash Banihashem, Atoosa Chegini +1
In data poisoning attacks, an adversary tries to change a model's prediction by adding, modifying, or removing samples in the training data. Recently, ensemble-based approaches for…