Continuous Optimization

Learning Graphons via Structured Gromov-Wasserstein Barycenters AAAI 2021 Learning local modules in dynamic networks L4DC 2021 Learning Multi-Scale Photo Exposure Correction CVPR 2021 Learning Stochastic Behaviour from Aggregate Data ICML 2021 Learning the Dynamics of Time Delay Systems with Trainable Delays L4DC 2021 Learning the optimal Tikhonov regularizer for inverse problems NeurIPS 2021 Learning to Learn Graph Topologies NeurIPS 2021 Learning with semi-definite programming: statistical bounds based on fixed point analysis and excess risk curvature JMLR 2021 Light RUMs ICML 2021 Linear Models are Robust Optimal Under Strategic Behavior AISTATS 2021 Local Hyper-Flow Diffusion NeurIPS 2021 Localization, Convexity, and Star Aggregation NeurIPS 2021 Localized Simple Multiple Kernel K-Means ICCV 2021 Localizing Changes in High-Dimensional Regression Models AISTATS 2021 Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NeurIPS 2021 Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems NeurIPS 2021 Low-Rank Sinkhorn Factorization ICML 2021 Low-Rank Tensor Completion by Approximating the Tensor Average Rank ICCV 2021 Machine learning structure preserving brackets for forecasting irreversible processes NeurIPS 2021 Making transport more robust and interpretable by moving data through a small number of anchor points ICML 2021 Matrix Completion with Model-free Weighting ICML 2021 Maximization of Monotone k-Submodular Functions with Bounded Curvature and Non-k-Submodular Functions ACML 2021 Measure Transport with Kernel Stein Discrepancy AISTATS 2021 Mirror Descent View for Neural Network Quantization AISTATS 2021 Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent AISTATS 2021