2 citations · 4 across the 6 of their papers we have counts for
8 papers
Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation
Ninh Pham, Rasmus Pagh
Approximation of non-linear kernels using random feature maps has become a powerful technique for scaling kernel methods to large datasets. We propose , an…
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
Ninh Pham, Tao Liu
We present Falconn++, a novel locality-sensitive filtering approach for approximate nearest neighbor search on angular distance. Falconn++ can filter out potential far away points…
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection
Kitty Li, Ninh Pham
In collaborative outlier detection, multiple participants exchange their local detectors trained on decentralized devices without exchanging their own data. A key problem of collab…
Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics
Ninh Pham
We propose a novel dimensionality reduction method for maximum inner product search (MIPS), named CEOs, based on the theory of concomitants of extreme order statistics. Utilizing t…
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search
Stephan S. Lorenzen, Ninh Pham
Top-k maximum inner product search (MIPS) is a central task in many machine learning applications. This paper extends top-k MIPS with a budgeted setting, that asks for the best app…
Hybrid LSH: Faster Near Neighbors Reporting in High-dimensional Space
Ninh Pham
We study the -near neighbors reporting problem (-NN), i.e., reporting \emph{all} points in a high-dimensional point set that lie within a radius of a given query poin…
Scalability and Total Recall with Fast CoveringLSH
Ninh Pham, Rasmus Pagh
Locality-sensitive hashing (LSH) has emerged as the dominant algorithmic technique for similarity search with strong performance guarantees in high-dimensional spaces. A drawback o…
I/O-Efficient Similarity Join
Rasmus Pagh, Ninh Pham, Francesco Silvestri +1
We present an I/O-efficient algorithm for computing similarity joins based on locality-sensitive hashing (LSH). In contrast to the filtering methods commonly suggested our method h…