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cs.LG2026
Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models
Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge
Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs remains poorly understood. In…
cs.LG2024
Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty
Xulin Chen, Ruipeng Liu, Zhenyu Gan +1
Uncertainties in transition dynamics pose a critical challenge in reinforcement learning (RL), often resulting in performance degradation of trained policies when deployed on hardw…