Adversarially Robust Multi-task Representation Learning
Authors: Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, Raman Arora
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study this problem from a theoretical perspective, building on prior work on this topic [13, 26, 12, 42]. We focus on transfer learning via learning a representation that provides a method for sharing knowledge between different, albeit related, tasks [7, 45, 14, 29, 49]. A common approach to achieve this has been termed multi-task representation learning (MTRL) [9, 24, 11]. In practice, the class of representations is a complex model like a deep neural network, and the predictors trained on top of them are simple linear models [7, 14]. Such a paradigm offers hope that we can pool our data, potentially providing a substantial benefit if performed well. |
| Researcher Affiliation | Collaboration | Austin Watkins Johns Hopkins University Baltimore, MD 21218 awatki29@jhu.edu Thanh Nguyen-Tang Johns Hopkins University Baltimore, MD 21218 nguyent@cs.jhu.edu Enayat Ullah Meta enayat@meta.com Raman Arora Johns Hopkins University Baltimore, MD 21218 arora@cs.jhu.edu |
| Pseudocode | Yes | Algorithm 1 (Two-stage adversarial MTRL). |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for the described methodology. It does not contain any links to code repositories or statements about code availability. |
| Open Datasets | No | The paper is purely theoretical and does not involve experimental data or datasets. Therefore, it does not specify public availability of datasets used for training. |
| Dataset Splits | No | The paper is purely theoretical and does not involve experimental data or datasets. Therefore, it does not specify training/test/validation dataset splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any experiments or the software dependencies with specific versions needed for reproduction. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |