HOT aSAX: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery. - Ninh D. Pham; Quang Loc Le; Tran Khanh Dang (ACIIDS (1), 2010)

Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases. - Ninh D. Pham; Quang Loc Le; Tran Khanh Dang (APWeb, 2010)

Online Discovery of Top-k Similar Motifs in Time Series Data. - Hoang Thanh Lam; Toon Calders; Ninh Pham (SDM, 2011)

A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data. - Ninh Pham; Rasmus Pagh (KDD, 2012)

Fast and scalable polynomial kernels via explicit feature maps. - Ninh Pham; Rasmus Pagh (KDD, 2013)

Efficient estimation for high similarities using odd sketches. - Michael Mitzenmacher; Rasmus Pagh; Ninh Pham (WWW, 2014)

Scalability and Total Recall with Fast CoveringLSH. - Ninh Pham; Rasmus Pagh (CIKM, 2016)

Hybrid LSH: Faster Near Neighbors Reporting in High-dimensional Space. - Ninh Pham (EDBT, 2017)

I/O-Efficient Similarity Join. - Rasmus Pagh; Ninh Pham; Francesco Silvestri 001; Morten Stöckel (Algorithmica, 2017)

L1-Depth Revisited: A Robust Angle-Based Outlier Factor in High-Dimensional Space. - Ninh Pham (ECML/PKDD (1), 2018)

Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search. - Stephan Sloth Lorenzen; Ninh Pham (ECML/PKDD (1), 2020)

Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics. - Ninh Pham (CoRR, 2020)

Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search (Extended Abstract). - Stephan Sloth Lorenzen; Ninh Pham (IJCAI, 2021)

Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics. - Ninh Pham (KDD, 2021)

Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search. - Ninh Pham; Tao Liu (NeurIPS, 2022)

An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection. - Kitty Li; Ninh Pham (CoRR, 2022)

A Transductive Forest for Anomaly Detection with Few Labels. - Jingrui Zhang; Ninh Pham; Gillian Dobbie (ECML/PKDD (1), 2023)

On Deploying Mobile Deep Learning to Segment COVID-19 PCR Test Tube Images. - Ting Xiang; Richard Dean; Jiawei Zhao; Ninh Pham (PSIVT, 2023)

Scalable DBSCAN with Random Projections. - Haochuan Xu; Ninh Pham (NeurIPS, 2024)

Scalable Density-based Clustering with Random Projections. - Haochuan Xu; Ninh Pham (CoRR, 2024)

On Finding Hubs in High Dimensions with Sampling. - Huiwen Dong; Linghan Zeng; Zhiwen Zhao; Francesco Silvestri 001; Ninh Pham (AAAI, 2025)

Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation. - Ninh Pham; Rasmus Pagh (CoRR, 2025)

Scalable Varied-Density Clustering via Graph Propagation. - Ninh Pham; Yingtao Zheng; Hugo Phibbs (CoRR, 2025)