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Found 5 papers, 1 papers with code
Game-theoretic Counterfactual Explanation for Graph Neural Networks
Our results reveals that computing Banzhaf values requires lower sample complexity in identifying the counterfactual explanations compared to other popular methods such as computing Shapley values.
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
Reinforcement learning generalizes multi-armed bandit problems with additional difficulties of a longer planning horizon and unknown transition kernel.
Fair Decision-Making for Food Inspections
Data and algorithms are essential and complementary parts of a large-scale decision-making process.
Combining No-regret and Q-learning
Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall.
Elicitation Complexity of Statistical Properties
We lay the foundation for a general theory of elicitation complexity, including several basic results about how elicitation complexity behaves, and the complexity of standard properties of interest.
