1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols
Mikhail Terekhov, Alexander Panfilov, Daniil Dzenhaliou +4
AI control protocols serve as a defense mechanism to stop untrusted LLM agents from causing harm in autonomous settings. Prior work treats this as a security problem, stress testin…
Control Tax: The Price of Keeping AI in Check
Mikhail Terekhov, Zhen Ning David Liu, Caglar Gulcehre +1
The rapid integration of agentic AI into high-stakes real-world applications requires robust oversight mechanisms. The emerging field of AI Control (AIC) aims to provide such an ov…
Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards
Daniil Karzanov, Rubén Garzón, Mikhail Terekhov +3
This paper introduces a novel agent-based approach for enhancing existing portfolio strategies using Proximal Policy Optimization (PPO). Rather than focusing solely on traditional…
Contextual Bandit Optimization with Pre-Trained Neural Networks
Mikhail Terekhov
Bandit optimization is a difficult problem, especially if the reward model is high-dimensional. When rewards are modeled by neural networks, sublinear regret has only been shown un…