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..
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Authors: Chris Wendler, Andisheh Amrollahi, Bastian Seifert, Andreas Krause, Markus Püschel10283-10292
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the two variants of our algorithm (SSFT and SSFT+) for model 4 on three classes of real-world set functions. |
| Researcher Affiliation | Academia | Department of Computer Science, ETH Zurich, Switzerland |
| Pseudocode | Yes | SSFT Sparse set function Fourier transform of s 1: M0 2: s 2M0 V ( ) s ( ) 3: for i = 1, . . . , n do ... 13: return s 2Mn V |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We construct two covariance matrices this way for temperature measurements from 46 sensors at Intel Research Berkeley and for velocity data from 357 sensors deployed under a highway in California. The networks stem from the Battle of Water Sensor Networks (BSWN) challenge (Ostfeld et al. 2008). Specifically, we use the multi-region valuation model (MRVM) from the spectrum auctions test suite (Weiss, Lubin, and Seuken 2017). |
| Dataset Splits | No | The paper does not explicitly provide percentages or sample counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions (e.g., Python 3.8, TensorFlow 2.x) that are needed to replicate the experiment. |
| Experiment Setup | Yes | For our algorithm we set ϵ = 0.001 and kmax = 1000. For CS-WHT we set... For H-WHT we used the exact algorithm... and set the expected sparsity parameter to 2000. For R-WHT we used the robust algorithm... and set the expected sparsity parameter to 2000 unless specified otherwise. |