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..
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Authors: Dongwoo Kim, Christian Walder
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on synthetic datasets as well as three real-world datasets, we show that the approximate matching PST results in better predictive performance than the other variants of PST. In Section 5 and 6, we verify our approach on synthetic datasets and demonstrate the improved predictive performance of our model on three real-world datasets. |
| Researcher Affiliation | Collaboration | 1Australian National University, Canberra, ACT, Australia 2Data to Decisions CRC, Kent Town, SA, Australia 3Data61 at CSIRO, Canberra, ACT, Australia. |
| Pseudocode | Yes | Algorithm 1 Online learning algorithm for unbounded a PST. and Algorithm 2 Online learning algorithm for self-bounded a PST. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use three datasets: a symbolic music dataset (Walder, 2016) from which we retain midi onset events only, a system call dataset (Hofmeyr et al., 1998), and human activity dataset (Ord onez et al., 2013). |
| Dataset Splits | Yes | For every experiment, we use the first 40% of a sequence to train, the subsequent 20% of the sequence to validate, and the final 40% of sequence to test the models. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For both parameter λ and ϵ, we test all possible configuration of λ = {2, 4, 6, 8, 10, 12}, ξ = (0.5, 0.7, 0.9, 0.99), and ϵ = {0, 1} and choose the best model based on the accuracy of validation set. |