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Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
As a prominent category of imitation learning methods, adversarial imitation learning (AIL) has garnered significant practical success powered by neural network approximation. However, existing theoretical studies on AIL are primarily limited to simplified scenarios such as tabular and linear function approximation and involve complex algorithmic designs that hinder practical implementation, highlighting a gap between theory and practice.
No-regret Algorithms for Fair Resource Allocation
Suppose a revenue-maximizing recommendation algorithm concludes from past data that more revenue is generated by showing the ad to Group A compared to Group B. In that case, the ad-serving algorithm will eventually end up showing that ad exclusively to Group A
Adaptive Exploration for Data-Efficient General Value Function Evaluations
General V alue Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning.
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Previous work has focused primarily on developing ML-based "meta-learners" that can provide point estimates of the conditional
Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation Jiaming Lv, Haoyuan Y ang
Since pioneering work of Hinton et al., knowledge distillation based on Kullback-Leibler Divergence (KL-Div) has been predominant, and recently its variants have
