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Found 15 papers, 4 papers with code
Generalization Bounds for Data-Driven Numerical Linear Algebra
Our techniques are general, and provide generalization bounds for many other recently proposed data-driven algorithms in numerical linear algebra, covering both sketching-based and multigrid-based methods.
Unveiling Transformers with LEGO: a synthetic reasoning task
We propose a synthetic task, LEGO (Learning Equality and Group Operations), that encapsulates the problem of following a chain of reasoning, and we study how the transformer architecture learns this task.
Triangle and Four Cycle Counting with Predictions in Graph Streams
We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature.
Few-Shot Data-Driven Algorithms for Low Rank Approximation
Recently, data-driven and learning-based algorithms for low rank matrix approximation were shown to outperform classical data-oblivious algorithms by wide margins in terms of accuracy.
Learning-based Support Estimation in Sublinear Time
We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a random sample of its elements.
Faster Kernel Matrix Algebra via Density Estimation
In particular, we consider estimating the sum of kernel matrix entries, along with its top eigenvalue and eigenvector.
Space and Time Efficient Kernel Density Estimation in High Dimensions
We instantiate our framework with the Laplacian and Exponential kernels, two popular kernels which possess the aforementioned property.
Scalable Nearest Neighbor Search for Optimal Transport
Our extensive experiments, on real-world text and image datasets, show that Flowtree improves over various baselines and existing methods in either running time or accuracy.
Data Structures and Algorithms
Sample-Optimal Low-Rank Approximation of Distance Matrices
Recent work by Bakshi and Woodruff (NeurIPS 2018) showed it is possible to compute a rank-$k$ approximation of a distance matrix in time $O((n+m)^{1+\gamma}) \cdot \mathrm{poly}(k, 1/\epsilon)$, where $\epsilon>0$ is an error parameter and $\gamma>0$ is an arbitrarily small constant.
Scalable Fair Clustering
In the fair variant of $k$-median, the points are colored, and the goal is to minimize the same average distance objective while ensuring that all clusters have an "approximately equal" number of points of each color.
Learning Space Partitions for Nearest Neighbor Search
Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms.
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
We consider the problem of labeling points on a fast-moving data stream when only a small number of labeled examples are available.
Practical Data-Dependent Metric Compression with Provable Guarantees
We introduce a new distance-preserving compact representation of multi-dimensional point-sets.
A graph-theoretic approach to multitasking
A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representations.
Volume Regularization for Binary Classification
We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes.
