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..
Multi-task Learning with Labeled and Unlabeled Tasks
Authors: Anastasia Pentina, Christoph H. Lampert
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also illustrate the effectiveness of the algorithm by experiments on synthetic and real data. |
| Researcher Affiliation | Academia | Anastasia Pentina 1 Christoph H. Lampert 1, 1IST Austria. Correspondence to: Anastasia Pentina <EMAIL>. |
| Pseudocode | Yes | Algorithm 1. 1. estimate pairwise discrepancies between the tasks based on the unlabeled data 2. choose the tasks I to be labeled (in the active case) and the weights α1, . . . , αT by minimizing (17) 3. receive labels for the labeled tasks I 4. for every task t train a classifier by minimizing (3) using the obtained weights αt. |
| Open Source Code | No | The paper links to a dataset (http://cvml.ist.ac.at/productreviews/) but does not provide an explicit statement or link for the source code of the described methodology. |
| Open Datasets | Yes | We curate a Multitask dataset of product reviews2 from the corpus of Amazon product data3 (Mc Auley et al., 2015a;b). 2http://cvml.ist.ac.at/productreviews/ 3http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | Yes | Regularization constants for all methods we selected from the set {0} {10 17, 10 16 . . . 108} by 5 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Python', 'Glo Ve word embedding', and various algorithms, but it does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | We use n = 1000 unlabeled and m = 100 labeled examples per task. ... We use n = 500 unlabeled samples per task and label a subset of m = 400 examples for each of the selected tasks. ... Regularization constants for all methods we selected from the set {0} {10 17, 10 16 . . . 108} by 5 5-fold cross validation. |