Noise-Contrastive Estimation for Multivariate Point Processes
Authors: Hongyuan Mei, Tom Wan, Jason Eisner
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 {hmei,tom,jason}@cs.jhu.edu |
| 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. |