16 citations · 45 across the 10 of their papers we have counts for
8 papers · 1 filter
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan +2
Agent trajectories record what an agent does and what happens next. Yet standard supervised fine-tuning (SFT) applies loss only to agent-authored action tokens, using environment o…
Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis
Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar +1
Membership inference attacks (MIAs) reveal whether specific data was used to train machine learning models, serving as important tools for privacy auditing and compliance assessmen…
Achieving Fairness Across Local and Global Models in Federated Learning
Disha Makhija, Xing Han, Joydeep Ghosh +1
Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attri…
Federated Learning for Estimating Heterogeneous Treatment Effects
Disha Makhija, Joydeep Ghosh, Yejin Kim
Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy…
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
Disha Makhija, Joydeep Ghosh, Nhat Ho
In several practical applications of federated learning (FL), the clients are highly heterogeneous in terms of both their data and compute resources, and therefore enforcing the sa…
Federated Self-supervised Learning for Heterogeneous Clients
Disha Makhija, Nhat Ho, Joydeep Ghosh
Federated Learning has become an important learning paradigm due to its privacy and computational benefits. As the field advances, two key challenges that still remain to be addres…