9 citations · 24 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Mazda Moayeri, Kiarash Banihashem, Soheil Feizi
Several existing works study either adversarial or natural distributional robustness of deep neural networks separately. In practice, however, models need to enjoy both types of ro…
Admissible Policy Teaching through Reward Design
Kiarash Banihashem, Adish Singla, Jiarui Gan +1
We study reward design strategies for incentivizing a reinforcement learning agent to adopt a policy from a set of admissible policies. The goal of the reward designer is to modify…
Defense Against Reward Poisoning Attacks in Reinforcement Learning
Kiarash Banihashem, Adish Singla, Goran Radanovic
We study defense strategies against reward poisoning attacks in reinforcement learning. As a threat model, we consider attacks that minimally alter rewards to make the attacker's t…