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A Conditional-Gradient-Based Augmented Lagrangian Framework.

, , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 7272-7281. PMLR, (2019)

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Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration., , and . CoRR, (2022)Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 4008-4017. PMLR, (2021)UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 772-795. PMLR, (2022)Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization., , , , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 8439-8457. PMLR, (2022)Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models., , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 18741-18753. PMLR, (2022)Model-based compressive sensing for signal ensembles., , and . Allerton, page 244-250. IEEE, (2009)Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition., , , , , , and . ACM Great Lakes Symposium on VLSI, page 433-438. ACM, (2016)Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps., , , and . SOSA, page 192-206. SIAM, (2023)A first-order primal-dual method with adaptivity to local smoothness., , and . NeurIPS, page 6171-6182. (2021)Finding Actual Descent Directions for Adversarial Training., , , , and . ICLR, OpenReview.net, (2023)