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Continuous Optimization

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Upper bounds for Model-Free Row-Sparse Principal Component Analysis ICML 2020
Utility/Privacy Trade-off through the lens of Optimal Transport AISTATS 2020
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case AISTATS 2020
Efficient Spectrum-Revealing CUR Matrix Decomposition AISTATS 2020
Regularity as Regularization: Smooth and Strongly Convex Brenier Potentials in Optimal Transport AISTATS 2020
A Graduated Filter Method for Large Scale Robust Estimation CVPR 2020
Learning to Optimize on SPD Manifolds CVPR 2020
What Does Plate Glass Reveal About Camera Calibration? CVPR 2020
Random extrapolation for primal-dual coordinate descent ICML 2020
Linearly Convergent Frank-Wolfe with Backtracking Line-Search AISTATS 2020
Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models AISTATS 2020
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization AISTATS 2020
Automatic Differentiation of Some First-Order Methods in Parametric Optimization AISTATS 2020
From Nesterov’s Estimate Sequence to Riemannian Acceleration COLT 2020
Dimension-Free Bounds for Chasing Convex Functions COLT 2020
Dynamic Regret of Convex and Smooth Functions NIPS 2020
Locally Accelerated Conditional Gradients AISTATS 2020
Joint learning of constraint weights and gradient inputs in Gradient Symbolic Computation with constrained optimization ACL 2020
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter AISTATS 2020
On the optimality of kernels for high-dimensional clustering AISTATS 2020
The Wasserstein Proximal Gradient Algorithm NIPS 2020
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks AISTATS 2020
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations NIPS 2020
Leverage Score Sampling for Faster Accelerated Regression and ERM ALT 2020
How to Trap a Gradient Flow COLT 2020