13 citations · 35 across the 8 of their papers we have counts for
4 papers · 1 filter
Personalized Projected Ascent for Federated Stackelberg Optimization
M Yashwanth, Sharannya Ghosh, Saikiran Bulusu +1
Federated minimax problems, such as distributionally robust optimization and adversarial training, include an inner maximization variable built from each client's local data. These…
Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation
M Yashwanth, Arunabh Singh, Ashok Nayak +2
Federated Learning (FL) algorithms implicitly assume that clients passively comply with server-side orchestration by sharing local model updates upon server request. However, this…
Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining
Daouda Sow, Herbert Woisetschläger, Saikiran Bulusu +3
Pretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current…
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li +2
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detect…