Mathematics & Optimization

Learning Some Popular Gaussian Graphical Models without Condition Number Bounds NeurIPS 2020 Learning sparse codes from compressed representations with biologically plausible local wiring constraints NeurIPS 2020 Learning Sparse Nonparametric DAGs AISTATS 2020 Learning Strategic Network Emergence Games NeurIPS 2020 Learning Strategy-Aware Linear Classifiers NeurIPS 2020 Learning Structured Distributions From Untrusted Batches: Faster and Simpler NeurIPS 2020 Learning Sums of Independent Random Variables with Sparse Collective Support JMLR 2020 Learning the Geometry of Wave-Based Imaging NeurIPS 2020 Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer AAAI 2020 Learning the piece-wise constant graph structure of a varying Ising model ICML 2020 Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising IJCAI 2020 Learning to Guide Random Search ICLR 2020 Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees AAAI 2020 Learning to Optimize on SPD Manifolds CVPR 2020 Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems AAAI 2020 Learning to Play Sequential Games versus Unknown Opponents NeurIPS 2020 Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting AAAI 2020 Learning to Select Base Classes for Few-Shot Classification CVPR 2020 Learning to Simulate and Design for Structural Engineering ICML 2020 Learning to Simulate Complex Physics with Graph Networks ICML 2020 Learning to solve TV regularised problems with unrolled algorithms NeurIPS 2020 Learning transport cost from subset correspondence ICLR 2020 Learning Triple Embeddings from Knowledge Graphs AAAI 2020 Learning URI Selection Criteria to Improve the Crawling of Linked Open Data (Extended Abstract) IJCAI 2020 Learning Utilities and Equilibria in Non-Truthful Auctions NeurIPS 2020