Statistical Learning
PiRank: Scalable Learning To Rank via Differentiable Sorting
The preference over items is specified via relevance labels for each candidate.
A Missing Details and Proofs We denote the degree of vertex v
We stress that unweighted and weighted in the linkage measure names refer to the linkage methods. Recall that our approach is based on geometric layering, where we group the edges based on their weights and process all edges within the same layer in parallel. A similar idea is used in the Affinity Clustering algorithm of Bateni et al. [ Our algorithm starts by first randomly coloring the active vertices red and blue with equal probability. Directly applying the random-mate approach (e.g., as applied in Let D be initialized to the identity clustering. O (log n) layers are required to represent every weight in this weight range.Lemma 2.1.
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Each step replaces the two most similar clusters by its union.
Supplementary Materials for " Generative vs Discriminative: Rethinking The Meta-Continual Learning "
In the above formulations, we assumed a uniform distribution on class labels for simplicity in notation. It is easy to generalize to the case of unbalanced class distributions.
Kernel Interpolation with Sparse Grids
These grids enable accurate interpolation, but with a number of points growing more slowly with dimension. We contribute a novel nearly linear time matrix-vector multiplication algorithm for the sparse grid kernel matrix.
