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Optimization

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Papers

Optimizing Percentile Criterion using Robust MDPs AISTATS 2021
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes AISTATS 2021
Anderson acceleration of coordinate descent AISTATS 2021
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence AISTATS 2021
Momentum Improves Optimization on Riemannian Manifolds AISTATS 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency AISTATS 2021
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression AISTATS 2021
Learning User Preferences in Non-Stationary Environments AISTATS 2021
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes AISTATS 2021
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms AISTATS 2021
Understanding Gradient Clipping In Incremental Gradient Methods AISTATS 2021
Learning Complexity of Simulated Annealing AISTATS 2021
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling AISTATS 2021
Minimax Model Learning AISTATS 2021
On the Faster Alternating Least-Squares for CCA AISTATS 2021
Iterative regularization for convex regularizers AISTATS 2021
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery AISTATS 2021
Explicit Regularization of Stochastic Gradient Methods through Duality AISTATS 2021
Gradient Descent in RKHS with Importance Labeling AISTATS 2021
Learning with Gradient Descent and Weakly Convex Losses AISTATS 2021
Approximate Data Deletion from Machine Learning Models AISTATS 2021
On the Suboptimality of Negative Momentum for Minimax Optimization AISTATS 2021
Corralling Stochastic Bandit Algorithms AISTATS 2021
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization AISTATS 2021
vqSGD: Vector Quantized Stochastic Gradient Descent AISTATS 2021