Adaptive Smoothed Online Multi-Task Learning
Authors: Keerthiram Murugesan, Hanxiao Liu, Jaime Carbonell, Yiming Yang
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over three multitask learning benchmark datasets show advantageous performance of the proposed approach over several state-of-the-art online multi-task learning baselines. We evaluate the performance of our algorithm under batch and online settings. All reported results in this section are averaged over 30 random runs or permutations of the training data. |
| Researcher Affiliation | Academia | Keerthiram Murugesan Carnegie Mellon University kmuruges@cs.cmu.edu Hanxiao Liu Carnegie Mellon University hanxiaol@cs.cmu.edu Jaime Carbonell Carnegie Mellon University jgc@cs.cmu.edu Yiming Yang Carnegie Mellon University yiming@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1: Batch Algorithm (SMTL-e) Algorithm 2: Online Algorithm (OSMTL-e) |
| Open Source Code | No | The paper does not provide a clear statement or link for the availability of its source code. |
| Open Datasets | Yes | Landmine Detection3 consists of 19 tasks collected from different landmine fields. [...] 3http://www.ee.duke.edu/~lcarin/Landmine_Data.zip Spam Detection4 We use the dataset obtained from ECML PAKDD 2006 Discovery challenge for the spam detection task. [...] 4http://ecmlpkdd2006.org/challenge.html Sentiment Analysis5 We evaluated our algorithm on product reviews from amazon. [...] 5http://www.cs.jhu.edu/~mdredze/datasets/sentiment |
| Dataset Splits | Yes | Unless otherwise specified, all model parameters are chosen via 5-fold cross validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU or CPU models) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | Corollary 2 provides a principled way to set hyperparameters to achieve the sub-linear regret bound. Specifically, recall α quantifies the self-concentration of each task. Therefore, α = T T implies for large horizon it would be less necessary to rely on other tasks as available supervision for the task itself is already plenty; C = 1+ T T T 0 suggests diminishing learning rate over the horizon length. Unless otherwise specified, all model parameters are chosen via 5-fold cross validation. |