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Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals.

, , , , , , и . J. Mach. Learn. Res., (2019)

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Sridharan Balakrishna

Karthik Arumugam

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Karthik Gururaj

 

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Online Learning: Sufficient Statistics and the Burkholder Method., , и . COLT, том 75 из Proceedings of Machine Learning Research, стр. 3028-3064. PMLR, (2018)Small-loss bounds for online learning with partial information., , и . COLT, том 75 из Proceedings of Machine Learning Research, стр. 979-986. PMLR, (2018)Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization., , и . ICML, icml.cc / Omnipress, (2012)On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization, , и . (2009)On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities., и . COLT, том 65 из Proceedings of Machine Learning Research, стр. 1704-1722. PMLR, (2017)BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits., и . ICML, том 48 из JMLR Workshop and Conference Proceedings, стр. 1977-1985. JMLR.org, (2016)Hypothesis Set Stability and Generalization., , , , , и . NeurIPS, стр. 6726-6736. (2019)Uniform Convergence of Gradients for Non-Convex Learning and Optimization., , и . NeurIPS, стр. 8759-8770. (2018)Online learning with dynamics: A minimax perspective., и . NeurIPS, (2020)Convex Games in Banach Spaces., и . COLT, стр. 1-13. Omnipress, (2010)