Differentiable Learning Under Triage
Authors: Nastaran Okati, Abir De, Manuel Rodriguez
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a wide variety of supervised learning tasks using synthetic and real data from two important applications content moderation and scientific discovery illustrate our theoretical results and show that the models and triage policies provided by our algorithm outperform those provided by several competitive baselines. |
| Researcher Affiliation | Academia | Nastaran Okati MPI for Software Systems nastaran@mpi-sws.org Abir De IIT Bombay abir@cse.iitb.ac.in Manuel Gomez-Rodriguez MPI for Software Systems manuelgr@mpi-sws.org |
| Pseudocode | Yes | Refer to Algorithm 1 for a pseudocode implementation of the overall gradient-based algorithm, which returns θT and γ. |
| Open Source Code | Yes | Our code and data are available at https://github.com/Networks-Learning/differentiable-learning-under-triage |
| Open Datasets | Yes | We use two publicly available datasets [33, 34], one from an application in content moderation and the other for scientific discovery. ... The Hatespeech dataset is publicly available under MIT license and the Galaxy zoo dataset is publicly available under Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 license. |
| Dataset Splits | Yes | In each experiment, we used 60% samples for training, 20% for validation and 20% for testing. |
| Hardware Specification | Yes | All algorithms were implemented in Python 3.7 and ran on a V100 Nvidia Tesla GPU with 32GB of memory. |
| Software Dependencies | No | The paper mentions 'Python 3.7' but does not list multiple key software components with specific version numbers, nor a self-contained solver with a version, which would be needed for a 'Yes' classification based on the schema requirements. |
| Experiment Setup | Yes | In each experiment, we used 60% samples for training, 20% for validation and 20% for testing. ... we find the parameters of the predictive model mθt via stochastic gradient descent (SGD) [28], i.e., θ(j) t = θ(j 1) t α(j 1) θ L... where α(j) is the learning rate at iteration j. |