no code implementations • 21 Jan 2024 • Frank Po-Chen Lin, Christopher Brinton
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters.
no code implementations • 9 May 2023 • Andrew Katz, Siqing Wei, Gaurav Nanda, Christopher Brinton, Matthew Ohland
This study contributes to the growing body of research on the use of AI models in educational contexts and highlights the potential of ChatGPT for facilitating analysis of student comments.
no code implementations • 25 Apr 2023 • JungHoon Kim, Taejoon Kim, David Love, Christopher Brinton
The design of codes for feedback-enabled communications has been a long-standing open problem.
no code implementations • 22 Mar 2023 • Frank Po-Chen Lin, Seyyedali Hosseinalipour, Nicolò Michelusi, Christopher Brinton
The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud.
no code implementations • 5 Dec 2022 • Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew Lan, Christopher Brinton
Traditional learning-based approaches to student modeling (e. g., predicting grades based on measured activities) generalize poorly to underrepresented/minority student groups due to biases in data availability.
no code implementations • 2 Aug 2022 • Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew Lan, Christopher Brinton
To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e. g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage.
1 code implementation • 28 Apr 2022 • Alexander Scarlatos, Christopher Brinton, Andrew Lan
One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention.
1 code implementation • ICLR 2022 • Sheikh Shams Azam, Seyyedali Hosseinalipour, Qiang Qiu, Christopher Brinton
In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning.
no code implementations • 29 Sep 2021 • Xin Zhang, Yanhua Li, Ziming Zhang, Christopher Brinton, Zhenming Liu, Zhi-Li Zhang, Hui Lu, Zhihong Tian
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
1 code implementation • 5 Oct 2020 • Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher Brinton
We study the problem of learning representations that are private yet informative, i. e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes.