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Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications such as recommendation systems.
Asymptotically efficient adaptive allocation rules
Tze Leung Lai and Herbert Robbins · 1985
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The Fast Fourier Transform and Its Applications
E. Oran Brigham · 1988
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Approximate nearest neighbors: Towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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Object recognition from local scale-invariant features
D.G. Lowe · 1999
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PAC Bounds for Multi-armed Bandit and Markov Decision Processes
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2002
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Video Google: A Text Retrieval Approach to Object Matching in Videos
J. Sivic and A. Zisserman · 2003
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Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2006
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Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
Eyal Even-Dar, Shie Mannor, and Yishay Mansour · 2006
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Training linear svms in linear time
Thorsten Joachims · 2006
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The Netflix Prize
James Bennett, Stan Lanning, and Netflix Netflix · 2007
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Approximate similarity search in metric spaces using inverted files
Giuseppe Amato and Pasquale Savino · 2008
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Effective proximity retrieval by ordering permutations
Edgar Chávez, Karina Figueroa, and Gonzalo Navarro · 2008
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Random projection trees and low dimensional manifolds
Sanjoy Dasgupta · 2008
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Active learning for large multi-class problems
Prateek Jain and Ashish Kapoor · 2009
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Cutting-plane training of structural svms
Thorsten Joachims, Thomas Finley, and Chun-Nam John Yu · 2009
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Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Empirical Bernstein Bounds and Sample Variance Penalization, 2009
Andreas Maurer and Massimiliano Pontil · 2009
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Best arm identification in multiarmed bandits
Jean-yves Audibert, Sébastien Bubeck, and Rémi Munos · 2010
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Performance of recommender algorithms on top-n recommendation tasks
Paolo Cremonesi, Yehuda Koren, and Roberto Turrin · 2010
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Approximate Nearest Neighbor Search Small World Approach
P. Alexander, Yury Malkov, L. Andrey, and V. Krylov · 2011
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Pure exploration in finitely-armed and continuous-armed bandits
Sébastien Bubeck, Rémi Munos, and Gilles Stoltz · 2011
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Product Quantization for Nearest Neighbor Search
Herve Jégou, Matthijs Douze, and Cordelia Schmid · 2011
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Searching in one billion vectors: Re-rank with source coding
Hervé Jégou, Romain Tavenard, Matthijs Douze, and Laurent Amsaleg · 2011
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The inverted multi-index
Artem Babenko and Victor Lempitsky · 2012
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Challenging the empirical mean and empirical variance: a deviation study
Olivier Catoni · 2012
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High-confidence near-duplicate image detection
Wei Dong, Zhe Wang, Moses Charikar, and Kai Li · 2012
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Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit
David L. Donoho, Yaakov Tsaig, Iddo Drori, and Jean-Luc Starck · 2012
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PAC subset selection in stochastic multi-armed bandits
Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, and Peter Stone · 2012
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Concentration Inequalities: A Nonasymptotic Theory of Independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Engineering efficient and effective non-metric space library
Leonid Boytsov and Bilegsaikhan Naidan · 2013
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Learning to prune in metric and non-metric spaces
Leonid Boytsov and Bilegsaikhan Naidan · 2013
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Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Thomas Dean, Mark A. Ruzon, Mark Segal, Jonathon Shlens, Sudheendra Vijayanarasimhan, and Jay Yagnik · 2013
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Optimized Product Quantization for Approximate Nearest Neighbor Search
Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun · 2013
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Almost optimal exploration in multi-armed bandits
Zohar Karnin, Tomer Koren, and Oren Somekh · 2013
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Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces
Yoram Bachrach, Yehuda Finkelstein, Ran Gilad-Bachrach, Liran Katzir, Noam Koenigstein, Nir Nice, and Ulrich Paquet · 2014
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Lil’ UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits
Kevin Jamieson, Matthew Malloy, Robert Nowak, and Sébastien Bubeck · 2014
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Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting
Kevin Jamieson and Robert Nowak · 2014
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Approximate nearest neighbor algorithm based on navigable small world graphs
Yury Malkov, Alexander Ponomarenko, Andrey Logvinov, and Vladimir Krylov · 2014
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Comparative Analysis of Data Structures for Approximate Nearest Neighbor Search
Alexander Ponomarenko, N. Avrelin, Bilegsaikhan Naidan, and Leonid Boytsov · 2014
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Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Anshumali Shrivastava and Ping Li · 2014
Cited alongside, same era.
Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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A bandit approach to maximum inner product search
Rui Liu, Tianyi Wu, and Barzan Mozafari · 2019
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On Efficient Retrieval of Top Similarity Vectors
Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, and Ping Li · 2019
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Local Orthogonal Decomposition for Maximum Inner Product Search
Xiang Wu, Ruiqi Guo, Sanjiv Kumar, and David Simcha · 2019
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Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Martin Zhang, James Zou, and David Tse · 2019
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Adaptive monte carlo multiple testing via multi-armed bandits
Martin Zhang, James Zou, and David Tse · 2019
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Anshumali Shrivastava and Ping Li · 2014
Cited alongside, same era.
Practical and optimal LSH for angular distance
Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, and Ludwig Schmidt · 2015
Cited alongside, same era.
Diamond Sampling for Approximate Maximum All-Pairs Dot-Product (MAD) Search
Grey Ballard, Tamara G. Kolda, Ali Pinar, and C. Seshadhri · 2015
Cited alongside, same era.
Concentration inequalities for sampling without replacement
Rémi Bardenet and Odalric-Ambrym Maillard · 2015
Cited alongside, same era.
The movielens datasets: History and context
F. Maxwell Harper and Joseph A. Konstan · 2015
Cited alongside, same era.
Query-aware locality-sensitive hashing for approximate nearest neighbor search
Qiang Huang, Jianlin Feng, Yikai Zhang, Qiong Fang, and Wilfred Ng · 2015
Cited alongside, same era.
Permutation search methods are efficient, yet faster search is possible
Bilegsaikhan Naidan, Leonid Boytsov, and Eric Nyberg · 2015
Cited alongside, same era.
Möbius Transformation for Fast Inner Product Search on Graph
Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, and Ping Li · 2019
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Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Xinyan Dai, Xiao Yan, Kelvin K. W. Ng, Jiu Liu, and James Cheng · 2020
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Accelerating large-scale inference with anisotropic vector quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar · 2020
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Understanding and Improving Proximity Graph Based Maximum Inner Product Search
Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, and Ming-Chang Yang · 2020
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Sublinear maximum inner product search using concomitants of extreme order statistics
Ninh Pham · 2020
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Sublinear Maximum Inner Product Search using Concomitants of Extreme Order Statistics
Ninh D. Pham · 2020
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Banditpam: Almost linear time k-medoids clustering via multi-armed bandits
Mo Tiwari, Martin J Zhang, James Mayclin, Sebastian Thrun, Chris Piech, and Ilan Shomorony · 2020
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Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?
Daichi Amagata and Takahiro Hara · 2021
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Bandit-based monte carlo optimization for nearest neighbors
Vivek Bagaria, Tavor Z Baharav, Govinda M Kamath, and N Tse David · 2021
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Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search (Extended Abstract)
Stephan S. Lorenzen and Ninh Pham · 2021
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AdaLSH: Adaptive LSH for Solving c
Kejing Lu and Mineichi Kudo · 2021
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Learning Sparse Binary Code for Maximum Inner Product Search
Changyi Ma, Fangchen Yu, Yueyao Yu, and Wenye Li · 2021
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Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics
Ninh Pham · 2021
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ProMIPS: Efficient High-Dimensional c-Approximate Maximum Inner Product Search with a Lightweight Index
Yang Song, Yu Gu, Rui Zhang, and Ge Yu · 2021
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Norm Adjusted Proximity Graph for Fast Inner Product Retrieval
Shulong Tan, Zhaozhuo Xu, Weijie Zhao, Hongliang Fei, Zhixin Zhou, and Ping Li · 2021
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GAIPS: Accelerating Maximum Inner Product Search with GPU
Long Xiang, Xiao Yan, Lan Lu, and Bo Tang · 2021
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Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures
Zhaozhuo Xu, Zhao Song, and Anshumali Shrivastava · 2021
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Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search
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Improving Language Models by Retrieving from Trillions of Tokens
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Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively
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H2SA-ALSH: A Privacy-Preserved Indexing and Searching Schema for IoT Data Collection and Mining
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