no code implementations • 3 Mar 2024 • Tiantian Feng, Anil Ramakrishna, Jimit Majmudar, Charith Peris, Jixuan Wang, Clement Chung, Richard Zemel, Morteza Ziyadi, Rahul Gupta
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns.
no code implementations • 23 Oct 2023 • Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continual stream of data without keeping the entire history.
1 code implementation • 19 May 2023 • Mustafa Safa Ozdayi, Charith Peris, Jack FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, Rahul Gupta
We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs.
no code implementations • 26 May 2022 • Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard Zemel
Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart.
no code implementations • NAACL 2022 • Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta
Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy.
no code implementations • 15 Oct 2021 • Jimit Majmudar, Stephen Vavasis
Graph clustering problems typically aim to partition the graph nodes such that two nodes belong to the same partition set if and only if they are similar.
no code implementations • NeurIPS 2020 • Jimit Majmudar, Stephen Vavasis
When the communities are allowed to overlap, often a pure nodes assumption is made, i. e. each community has a node that belongs exclusively to that community.