Multi-task Learning with Labeled and Unlabeled Tasks
Authors: Anastasia Pentina, Christoph H. Lampert
ICML 2017 | Conference PDF | Archive PDF | Plain Text | 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 <apentina@ist.ac.at>. |
| 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. |