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Composable Coresets for Determinant Maximization: Greedy is Almost Optimal
However, the most common algorithm used for this problem in practical applications is a natural greedy algorithm.
QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Causal discovery is essential for understanding relationships among variables of interest in many scientific domains. In this paper, we focus on permutation-based methods for learning causal graphs in Linear Gaussian Acyclic Models (LiGAMs), where the permutation encodes a causal ordering of the variables. Existing methods in this setting do not scale due to their high computational complexity.
Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
It also employs a new shuffling estimator for the "hyper-gradient",
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement (Appendix) A Reference point and hypervolume ratio
In the inference process, the submodel is used to solve the corresponding subproblem. The input dimensions of the node features vary with different problems. A masking mechanism is adopted in each decoding step to ensure the solution feasibility. For MOTSP, the visited nodes are masked. NHDE-M usually spends relatively more inference time than MDRL with the same number of weights.