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..
Repelling Random Walks
Authors: Isaac Reid, Eli Berger, Krzysztof Marcin Choromanski, Adrian Weller
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide detailed experimental evaluation and robust theoretical guarantees. |
| Researcher Affiliation | Collaboration | Isaac Reid1 , Eli Berger2 , Krzysztof Choromanski3,4 , Adrian Weller1,5 1University of Cambridge, 2University of Haifa, 3Google Deep Mind, 4Columbia University, 5Alan Turing Institute |
| Pseudocode | Yes | Algorithm 4.1 (Random walks for Page Rank estimation). (Fogaras et al., 2005) Simulate m N runs of a simple random walk with transition probability matrix P out of every node i N, terminating with probability p at each timestep. Evaluate the estimator bĪj as the fraction of walks terminating at node j, bĪj := 1 Nm P N Pm j=1 I(walker terminates at j). |
| Open Source Code | Yes | Code is available at https://github.com/isaac-reid/repelling_random_walks. |
| Open Datasets | Yes | All datasets used correspond to standard graphs and are freely available online; we give links to suitable repositories in every instance. |
| Dataset Splits | No | The paper mentions training and testing data splits (e.g., 5% test split for kernel regression) but does not specify any validation splits or percentages across its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, specific library versions) that would be needed for replication. |
| Experiment Setup | Yes | We take 100 repeats to compute the variance of the kernel approximation error, using a regulariser Ī = 0.1 and a termination probability p = 0.5. [...] We use m = 16 random walks with a termination probability p = 0.5 and a regulariser Ī = 0.1, taking 1000 repeats for statistics. [...] The termination probability is p = 0.3 and we take 1000 trials to compute the standard deviations (10000 for eurosis since it is larger). [...] We impose a restricted sampling budget with walks of length L = 16 to highlight the benefits of repelling random walks in the transient regime, and take 2500 repeats over all starting nodes for statistics. |