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Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains., , , and . CoRR, (2019)Convergence rates for pretraining and dropout: Guiding learning parameters using network structure., , and . CoRR, (2015)Robust Blind Deconvolution via Mirror Descent., , and . CoRR, (2018)FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret., , , and . ECCV (12), volume 12357 of Lecture Notes in Computer Science, page 365-381. Springer, (2020)Accelerated Neural Network Training with Rooted Logistic Objectives., , , and . CoRR, (2023)Generating Accurate Pseudo-labels via Hermite Polynomials for SSL Confidently., , , , and . CoRR, (2019)A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees., , and . CoRR, (2017)Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data., , , , and . CoRR, (2022)A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees., , and . INFORMS J. Optim., 1 (2): 120-142 (April 2019)You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling., , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 12321-12332. PMLR, (2021)