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..
Noise-Contrastive Estimation for Multivariate Point Processes
Authors: Hongyuan Mei, Tom Wan, Jason Eisner
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On several synthetic and real-world datasets, our method shows benefits: for the model to achieve the same level of log-likelihood on held-out data, our method needs considerably fewer function evaluations and less wall-clock time. and 5 Experiments We evaluate our NCE method on several synthetic and real-world datasets, with comparison to MLE, Guo et al. (2018) (denoted as b-NCE), and least-squares estimation (LSE) (Eichler et al., 2017). |
| Researcher Affiliation | Academia | Hongyuan Mei Tom Wan Jason Eisner Department of Computer Science, Johns Hopkins University 3400 N. Charles Street, Baltimore, MD 21218 U.S.A EMAIL |
| Pseudocode | Yes | The full pseudocode is given in Algorithm 1 in the supplementary material. |
| Open Source Code | Yes | By describing this method and releasing code, we hope to facilitate probabilistic modeling of continuous-time sequential data in many domains. |
| Open Datasets | Yes | Euro Email (Paranjape et al., 2017). This dataset contains time-stamped emails between anonymized members of a European research institute. We work on a subset of 100 most active members and then end up with K = 10000 possible event types and 50000 training event tokens. and Bitcoin OTC (Kumar et al., 2016). This dataset contains time-stamped rating (positive/negative) records between anonymized users on the Bitcoin OTC trading platform. We work on a subset of 100 most active users and then end up with K = 19800 (self-rating not allowed) possible event types but only 1000 training event tokens: this is an extremely data-sparse setting. and Robo Cup (Chen & Mooney, 2008). and IPTV (Xu et al., 2018). |
| Dataset Splits | Yes | For all the datasets, we use the first 80% as the training set, the next 10% as the validation set, and the last 10% as the test set. |
| Hardware Specification | Yes | two Titan X Pascal GPUs donated by NVIDIA Corporation and compute cycles from the Maryland Advanced Research Computing Center. |
| Software Dependencies | No | The paper mentions software like PyTorch and the Adam optimizer, but does not provide specific version numbers for these or other software dependencies used in the experiments. |
| Experiment Setup | Yes | We always set the minibatch size B to exhaust the GPU capacity, so smaller ρ or M allows larger B. and The displayed ρ and M values are among the better ones that we found during hyperparameter search. |