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The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models.

, , , and . NeurIPS, (2020)

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Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 1753-1800. PMLR, (2023)Kernel Conditional Moment Constraints for Confounding Robust Inference., and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 650-674. PMLR, (2023)Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents., , , and . CoRR, (2019)Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation., , , and . IEEE Trans. Autom. Control., 68 (5): 2891-2905 (May 2023)Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization., , , and . CoRR, (2023)Provably Convergent Policy Optimization via Metric-aware Trust Region Methods., , , and . Trans. Mach. Learn. Res., (2023)Parameter-Agnostic Optimization under Relaxed Smoothness., , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 4861-4869. PMLR, (2024)Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence., and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 3493-3501. PMLR, (2024)Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles., , , and . CoRR, (2024)Optimization for Reinforcement Learning: From a single agent to cooperative agents., , , and . IEEE Signal Process. Mag., 37 (3): 123-135 (2020)