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Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach.

, , , , , and . NeurIPS, (2020)

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Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach., , , , , and . NeurIPS, (2020)Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss., , , , and . NeurIPS, (2020)Exponential Family Model-Based Reinforcement Learning via Score Matching., , , , , and . NeurIPS, (2022)Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL., , , , , and . NeurIPS, page 17913-17926. (2021)Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium., , , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 3674-3682. PMLR, (2020)Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games., , , and . ICLR, OpenReview.net, (2020)Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory., , , and . CoRR, (2024)A General Framework for Sequential Decision-Making under Adaptivity Constraints., , and . ICML, OpenReview.net, (2024)Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers., , , and . CoRR, (2024)Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes., , , and . ICLR, OpenReview.net, (2023)