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Found 8 papers, 2 papers with code
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search
In particular, our new routing methods enable the use of balanced graph partitioning, which is a high-quality partitioning method without a naturally associated routing algorithm.
A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP hierarchies
Multi-dimensional Scaling (MDS) is a family of methods for embedding an $n$-point metric into low-dimensional Euclidean space.
HyperAttention: Long-context Attention in Near-Linear Time
Recent work suggests that in the worst-case scenario, quadratic time is necessary unless the entries of the attention matrix are bounded or the matrix has low stable rank.
A Near-Linear Time Algorithm for the Chamfer Distance
For any two point sets $A, B \subset \mathbb{R}^d$ of size up to $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A, B)=\sum_{a \in A} \min_{b \in B} d_X(a, b)$, where $d_X$ is the underlying distance measure (e. g., the Euclidean or Manhattan distance).
Stars: Tera-Scale Graph Building for Clustering and Graph Learning
We evaluate the performance of Stars for clustering and graph learning, and demonstrate 10~1000-fold improvements in pairwise similarity comparisons compared to different baselines, and 2~10-fold improvement in running time without quality loss.
Learning and Testing Junta Distributions with Subcube Conditioning
The main contribution is an algorithm for finding relevant coordinates in a $k$-junta distribution with subcube conditioning [BC18, CCKLW20].
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
For input $\mathcal{A}$ as above, we give $O(\sum_{i=1}^q \text{nnz}(A_i))$ time algorithms, which is much faster than computing $\mathcal{A}$.
Learning Two Layer Rectified Neural Networks in Polynomial Time
Given $n$ samples as a matrix $\mathbf{X} \in \mathbb{R}^{d \times n}$ and the (possibly noisy) labels $\mathbf{U}^* f(\mathbf{V}^* \mathbf{X}) + \mathbf{E}$ of the network on these samples, where $\mathbf{E}$ is a noise matrix, our goal is to recover the weight matrices $\mathbf{U}^*$ and $\mathbf{V}^*$.
