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..
Adversarially Robust Multi-task Representation Learning
Authors: Austin Watkins, Thanh Nguyen-Tang, Enayat Ullah, Raman Arora
NeurIPS 2024 | Venue PDF | 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 EMAIL Thanh Nguyen-Tang Johns Hopkins University Baltimore, MD 21218 EMAIL Enayat Ullah Meta EMAIL Raman Arora Johns Hopkins University Baltimore, MD 21218 EMAIL |
| 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. |