Distributed Primal-Dual Optimization for Online Multi-Task Learning
Authors: Peng Yang, Ping Li6631-6638
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results confirm that the proposed model is highly effective on various real-world datasets. Empirical experiments are conducted to evaluate the algorithms on three datasets used in previous work (Zhang et al. 2018). |
| Researcher Affiliation | Industry | Peng Yang, Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th ST, Bellevue WA, 98004, USA {pengyang01, liping11}@baidu.com |
| Pseudocode | Yes | Algorithm 1 DROM: Distributed Primal-dual optimization for Online MTL |
| Open Source Code | No | The paper states that implementations for baselines are provided in 'Supporting Materials' but does not explicitly state that the source code for their proposed method (DROM/DROM-D) is open-source or provide a link. |
| Open Datasets | Yes | Spam Email2 contains 7,068 emails collected from mailboxes of 4 users (i.e., 4 tasks). (Footnote 2: http://labs-repos.iit.demokritos.gr/skel/i-config/) MHC-I3, a bio-marker dataset, contains 18,664 peptide sequences for 12 MHC-I molecules (i.e., 12 tasks). (Footnote 3: http://web.cs.iastate.edu/~honar/ailab/) Each Movie4 is a movie recommendation dataset where 72,916 users rate a subset of 1,628 movies. (Footnote 4: http://goldberg.berkeley.edu/jester-data/) |
| Dataset Splits | No | The paper describes an online learning setting and evaluates performance using cumulative error rate over online data but does not provide specific train/validation/test dataset splits or their sizes. |
| Hardware Specification | No | The paper reports runtime in seconds but does not provide any specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For both methods, we simply set λ = 1, ρ = 1 to avoid overfitting, and tune p (0, 1) with ξ = 1 to deal with adversarial noise. When ηt = 1/ t the following regret is hold (for DROM) and When ηt = 1/ t/τ , the regret holds (for DROM-D). |