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cs.LG2022
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests
Erica Cai, Andrew McGregor, David Jensen
Learning causal structure is useful in many areas of artificial intelligence, including planning, robotics, and explanation. Constraint-based structure learning algorithms such as…
cs.LG2022
Measuring Interventional Robustness in Reinforcement Learning
Katherine Avery, Jack Kenney, Pracheta Amaranath +2
Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability…