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cs.LG2026
Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
Mohit Prashant, Arvind Easwaran
Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibili…
cs.LG2026
Approximation-Free Differentiable Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabular data. However, training ac…