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Found 11 papers, 3 papers with code
Efficient and Local Parallel Random Walks
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning.
Spectral Clustering Oracles in Sublinear Time
The main technical contribution is a sublinear time oracle that provides dot product access to the spectral embedding of $G$ by estimating distributions of short random walks from vertices in $G$.
Data Structures and Algorithms
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Random binning features, introduced in the seminal paper of Rahimi and Recht (2007), are an efficient method for approximating a kernel matrix using locality sensitive hashing.
Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems
We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases.
Distributed, Parallel, and Cluster Computing Databases Data Structures and Algorithms Performance
Efficiently Learning Fourier Sparse Set Functions
In this paper we consider the problem of efficiently learning set functions that are defined over a ground set of size $n$ and that are sparse (say $k$-sparse) in the Fourier domain.
Oblivious Sketching of High-Degree Polynomial Kernels
Oblivious sketching has emerged as a powerful approach to speeding up numerical linear algebra over the past decade, but our understanding of oblivious sketching solutions for kernel matrices has remained quite limited, suffering from the aforementioned exponential dependence on input parameters.
Data Structures and Algorithms
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms
We formalize this intuition by showing that, roughly, a continuous signal from a given class can be approximately reconstructed using a number of samples proportional to the *statistical dimension* of the allowed power spectrum of that class.
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Qualitatively, our results are twofold: on the one hand, we show that random Fourier feature approximation can provably speed up kernel ridge regression under reasonable assumptions.
Prediction strategies without loss
Moreover, for {\em any window of size $n$} the regret of our algorithm to any expert never exceeds $O(\sqrt{n(\log N+\log T)})$, where $N$ is the number of experts and $T$ is the time horizon, while maintaining the essentially zero loss property.
Factor Modeling for Advertisement Targeting
We adapt a probabilistic latent variable model, namely GaP (Gamma-Poisson), to ad targeting in the contexts of sponsored search (SS) and behaviorally targeted (BT) display advertising.
Perfect Matchings in O(n \log n) Time in Regular Bipartite Graphs
Our techniques also give an algorithm that successively finds a matching in the support of a doubly stochastic matrix in expected time O(n\log^2 n) time, with O(m) pre-processing time; this gives a simple O(m+mn\log^2 n) time algorithm for finding the Birkhoff-von Neumann decomposition of a doubly stochastic matrix.
Data Structures and Algorithms Discrete Mathematics
