3 papers
cs.LG2026
Rarity-Gated Context Conditioning for Offline Imitation Learning-Based Maritime Anomaly Detection
Yongmin Kim, ByeongHoon Jeon, Sungil Kim
Contextual anomaly detection aims to identify abnormal behavior conditional on context variables, but practical deployments often face highly imbalanced context distributions where…
cs.LG2026
OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimation
Paavo Parmas, Yongmin Kim, Kohsei Matsutani +5
Policy-gradient methods usually optimize expected return, but many real world applications care about distributional properties of returns: tail risk, outlier robustness, or best-o…
cs.LG2026
On Advantage Estimates for Max@K Policy Gradients
Shota Takashiro, Soichiro Nishimori, Paavo Parmas +6
Reinforcement learning with verifiable rewards is widely used for post-training reasoning models, but sparse outcome rewards make exploration difficult. A complementary approach is…