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Optimization

1,207 papers

Papers per year

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Papers

On Performance Estimation in Automatic Algorithm Configuration AAAI 2020
Cakewalk Sampling AAAI 2020
Regression under Human Assistance AAAI 2020
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees AAAI 2020
A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020
Robust Tensor Decomposition via Orientation Invariant Tubal Nuclear Norms AAAI 2020
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes AAAI 2020
Submodel Decomposition for Solving Limited Memory Influence Diagrams (Student Abstract) AAAI 2020
Selecting Portfolios Directly Using Recurrent Reinforcement Learning (Student Abstract) AAAI 2020
A Fast Anderson-Chebyshev Acceleration for Nonlinear Optimization AISTATS 2020
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach AISTATS 2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout AISTATS 2020
Causal Bayesian Optimization AISTATS 2020
Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020
Revisiting Stochastic Extragradient AISTATS 2020
Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models COLT 2020
List Decodable Subspace Recovery COLT 2020
Bundle Adjustment on a Graph Processor CVPR 2020
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator CVPR 2020
TomoFluid: Reconstructing Dynamic Fluid From Sparse View Videos CVPR 2020
Correspondence-Free Material Reconstruction using Sparse Surface Constraints CVPR 2020
Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging CVPR 2020
gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors CVPR 2020
Auto-Tuning Structured Light by Optical Stochastic Gradient Descent CVPR 2020
Cogradient Descent for Bilinear Optimization CVPR 2020