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Bocconi University, Milan, Italy
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Learning $\max$@$k$-optimal policies is statistically harder than traditional reinforcement learning, revealing a critical gap in agent evaluation methods.
Computing approximate stationary points in min-max optimization for quadratic polynomials is proven to be PPAD-hard, revealing deep implications for game theory and optimization.
