Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Faster Maximum Inner Product Search in High Dimensions
Authors: Mo Tiwari, Ryan Kang, Jaeyong Lee, Donghyun Lee, Christopher J Piech, Sebastian Thrun, Ilan Shomorony, Martin Jinye Zhang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the scaling of Bandit MIPS and demonstrate that Bandit MIPS outperforms prior state-of-the-art MIPS algorithms in sample complexity, wall-clock time, and precision/speedup tradeoff across a variety of experimental settings. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, Stanford, CA 2Oxford University 3University College London 4Electrical and Computer Engineering, University of Illinois at Urbana-Champaign 5Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University. |
| Pseudocode | Yes | Algorithm 1 Bandit MIPS |
| Open Source Code | Yes | All of our experimental results are reproducible via a 1-line script at github.com/Thrun Group/Bandit MIPS. |
| Open Datasets | Yes | Datasets: We empirically evaluate the performance of Bandit MIPS and Bandit MIPS-α on two synthetic and four realworld datasets... Netflix Prize dataset (Bennett et al., 2007) the Movie Lens dataset (Harper & Konstan, 2015), the Sift-1M (J egou et al., 2011) and Crypto Pairs datasets (Carsten, 2022) |
| Dataset Splits | No | The paper discusses varying hyperparameters and error probabilities (e.g., δ and ϵ) to obtain speedups and trade-offs, and mentions using low-rank approximations or NMF for missing values, but it does not specify explicit training/validation/test dataset splits (e.g., in percentages or sample counts) for reproducibility. |
| Hardware Specification | Yes | All experiments were run on a 2019 Macbook Pro with a 2.4 GHz 8-Core Intel Core i9 CPU, 64 GB 2667 MHz DDR4 RAM, and an Intel UHD Graphics 630 1536 MB graphics card. |
| Software Dependencies | No | The paper provides a GitHub link implying code availability but does not specify any software names with version numbers, such as Python versions or specific libraries like PyTorch or scikit-learn. |
| Experiment Setup | Yes | In all scaling experiments, δ and ϵ were both set to 0.001 for Bandit MIPS and Bandit MIPS-α. For the NORMAL CUSTOM and CORRELATED NORMAL CUSTOM datasets, the sub-Gaussianity parameter was set to σ = 1. For the Netflix and Movie Lens datasets, the sub-Gaussianity parameter was set to σ = 25. For the Crypto Pairs, SIFT-1M, and Simple Song datasets described in Appendix D, the sub-Gaussianity parameters were set to σ = 2.5e9, σ = 6.25e5, and σ = 25, respectively... For the precision versus speedup tradeoff experiments, the number of dimensions was fixed to d = 10,000. The various values of speedups were obtained by varying the hyperparameters of each algorithm. For NAPG-MIPS and HNSW-MIPS, for example, M was varied from 4 to 32, ef constructions was varied from 2 to 500, and ef searches was varied from 2 to 500. For Greedy-MIPS, budget varied from 2 to 999. For LSH-MIPS, the number of hash functions and hash values vary from 1 to 10. For H2ALSH, δ varies from 1 24 to 1 2, c0 varies from 1.2 to 5, and c varies from 0.9 to 2. For Bandit MIPS , Bandit MIPS-α, and Bounded ME, speedups were obtained by varying δ from 1 1010 to 0.99 and ϵ from 1 1010 to 3. |