53 citations · 87 across the 25 of their papers we have counts for
5 papers · 1 filter
Cost-Minimized Label-Flipping Poisoning Attack to LLM Alignment
Shigeki Kusaka, Keita Saito, Mikoto Kudo +3
Large language models (LLMs) are increasingly deployed in real-world systems, making it critical to understand their vulnerabilities. While data poisoning attacks during RLHF/DPO a…
A Provable Approach for End-to-End Safe Reinforcement Learning
Akifumi Wachi, Kohei Miyaguchi, Takumi Tanabe +2
A longstanding goal in safe reinforcement learning (RL) is a method to ensure the safety of a policy throughout the entire process, from learning to operation. However, existing sa…
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies
Runze Yan, Xun Shen, Akifumi Wachi +3
When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropriate generalization beyond clini…
Target Return Optimizer for Multi-Game Decision Transformer
Kensuke Tatematsu, Akifumi Wachi
Achieving autonomous agents with robust generalization capabilities across diverse games and tasks remains one of the ultimate goals in AI research. Recent advancements in transfor…
Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing
Thien Q. Tran, Akifumi Wachi, Rei Sato +2
Safety alignment is an essential research topic for real-world AI applications. Despite the multifaceted nature of safety and trustworthiness in AI, current safety alignment method…