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 | Conference PDF | Archive PDF | Plain Text | 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. |