Optimization
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering Ines Chami Albert Gu Vaggos Chatziafratis Christopher Ré
In this work, we provide the first continuous relaxation of Dasgupta's discrete optimization problem with provable quality guarantees.
Learning Feature Sparse Principal Subspace
(Algorithm 1). Then, we propose another strategy (Algorithm 2) to solve FSPCA for the general covariance by iteratively building a carefully designed proxy.
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach Bo Liu
BO is useful in a variety of machine learning tasks.
Appendix A Additional table
Table 2 presents the numerical results for the ablation study in Section 4.2. The results of our main method in Section 4.1 is reported in column Main. Table 3 provides additional ablation study on several building blocks of our main method. T est denotes the variant of using the estimated reward function as the test function when We see that changing the proposed JSD regularizer in Section 3.2 to the KL-dual-based regularizers Changing the implicit policy to the Gaussian policy generally leads to worse performance. The performance difference is especially significant on the Maze2D and Adroit datasets.